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Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT Scientific Publishing, Karlsruhe.","chicago":"Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” In Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut, 135–46. KIT Scientific Publishing, Karlsruhe, 2019.","ieee":"A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking,” in Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, Dortmund, 2019, pp. 135–146.","short":"A. Tornede, M.D. Wever, E. Hüllermeier, in: F. Hoffmann, E. 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Henzgen and E. Hüllermeier, “Mining Rank Data,” ACM Transactions on Knowledge Discovery from Data, pp. 1–36, 2019.","short":"S. Henzgen, E. Hüllermeier, ACM Transactions on Knowledge Discovery from Data (2019) 1–36.","mla":"Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” ACM Transactions on Knowledge Discovery from Data, 2019, pp. 1–36, doi:10.1145/3363572.","bibtex":"@article{Henzgen_Hüllermeier_2019, title={Mining Rank Data}, DOI={10.1145/3363572}, journal={ACM Transactions on Knowledge Discovery from Data}, author={Henzgen, Sascha and Hüllermeier, Eyke}, year={2019}, pages={1–36} }","chicago":"Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” ACM Transactions on Knowledge Discovery from Data, 2019, 1–36. https://doi.org/10.1145/3363572.","ama":"Henzgen S, Hüllermeier E. Mining Rank Data. ACM Transactions on Knowledge Discovery from Data. 2019:1-36. doi:10.1145/3363572","apa":"Henzgen, S., & Hüllermeier, E. (2019). Mining Rank Data. 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Journal of Multivariate Analysis, 291–312. https://doi.org/10.1016/j.jmva.2019.02.017","chicago":"Bengs, Viktor, Matthias Eulert, and Hajo Holzmann. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.” Journal of Multivariate Analysis, 2019, 291–312. https://doi.org/10.1016/j.jmva.2019.02.017.","mla":"Bengs, Viktor, et al. “Asymptotic Confidence Sets for the Jump Curve in Bivariate Regression Problems.” Journal of Multivariate Analysis, 2019, pp. 291–312, doi:10.1016/j.jmva.2019.02.017.","bibtex":"@article{Bengs_Eulert_Holzmann_2019, title={Asymptotic confidence sets for the jump curve in bivariate regression problems}, DOI={10.1016/j.jmva.2019.02.017}, journal={Journal of Multivariate Analysis}, author={Bengs, Viktor and Eulert, Matthias and Holzmann, Hajo}, year={2019}, pages={291–312} }","short":"V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019) 291–312.","ieee":"V. Bengs, M. Eulert, and H. Holzmann, “Asymptotic confidence sets for the jump curve in bivariate regression problems,” Journal of Multivariate Analysis, pp. 291–312, 2019."},"year":"2019","type":"journal_article","page":"291-312","_id":"14027","date_updated":"2022-01-06T06:51:52Z","doi":"10.1016/j.jmva.2019.02.017"},{"_id":"14028","date_updated":"2022-01-06T06:51:52Z","doi":"10.1214/19-ejs1555","language":[{"iso":"eng"}],"page":"1523-1579","type":"journal_article","year":"2019","citation":{"apa":"Bengs, V., & Holzmann, H. (2019). Adaptive confidence sets for kink estimation. Electronic Journal of Statistics, 1523–1579. https://doi.org/10.1214/19-ejs1555","ama":"Bengs V, Holzmann H. Adaptive confidence sets for kink estimation. Electronic Journal of Statistics. 2019:1523-1579. doi:10.1214/19-ejs1555","chicago":"Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” Electronic Journal of Statistics, 2019, 1523–79. https://doi.org/10.1214/19-ejs1555.","bibtex":"@article{Bengs_Holzmann_2019, title={Adaptive confidence sets for kink estimation}, DOI={10.1214/19-ejs1555}, journal={Electronic Journal of Statistics}, author={Bengs, Viktor and Holzmann, Hajo}, year={2019}, pages={1523–1579} }","mla":"Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.” Electronic Journal of Statistics, 2019, pp. 1523–79, doi:10.1214/19-ejs1555.","short":"V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579.","ieee":"V. Bengs and H. Holzmann, “Adaptive confidence sets for kink estimation,” Electronic Journal of Statistics, pp. 1523–1579, 2019."},"user_id":"76599","title":"Adaptive confidence sets for kink estimation","publication":"Electronic Journal of Statistics","department":[{"_id":"34"},{"_id":"355"}],"author":[{"full_name":"Bengs, Viktor","first_name":"Viktor","id":"76599","last_name":"Bengs"},{"full_name":"Holzmann, Hajo","first_name":"Hajo","last_name":"Holzmann"}],"date_created":"2019-10-30T14:25:16Z","status":"public","publication_identifier":{"issn":["1935-7524"]},"publication_status":"published"},{"title":"From Automated to On-The-Fly Machine Learning","user_id":"38209","place":"Bonn","status":"public","date_created":"2019-09-04T08:44:46Z","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"publisher":"Gesellschaft für Informatik e.V.","author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"department":[{"_id":"355"}],"publication":"INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft","date_updated":"2022-01-06T06:51:28Z","_id":"13132","conference":{"end_date":"2019-09-26","location":"Kassel","start_date":"2019-09-23","name":"Informatik 2019"},"citation":{"bibtex":"@inproceedings{Mohr_Wever_Tornede_Hüllermeier_2019, place={Bonn}, series={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik}, title={From Automated to On-The-Fly Machine Learning}, booktitle={INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, publisher={Gesellschaft für Informatik e.V.}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier, Eyke}, year={2019}, pages={273–274}, collection={INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik} }","mla":"Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., 2019, pp. 273–74.","chicago":"Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier. “From Automated to On-The-Fly Machine Learning.” In INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, 273–74. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft für Informatik e.V., 2019.","apa":"Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft für Informatik e.V.","ama":"Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.","ieee":"F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.","short":"F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft für Informatik e.V., Bonn, 2019, pp. 273–274."},"type":"conference_abstract","year":"2019","page":" 273-274 ","language":[{"iso":"eng"}],"series_title":"INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik"},{"user_id":"33176","ddc":["006"],"title":"Automating Multi-Label Classification Extending ML-Plan","abstract":[{"lang":"eng","text":"Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn, and more recently ML-Plan, have shown impressive results for the tasks of single-label classification and regression. Yet, there is only little work on other types of machine learning problems so far. In particular, there is almost no work on automating the engineering of machine learning solutions for multi-label classification (MLC). We show how the scope of ML-Plan, an AutoML-tool for multi-class classification, can be extended towards MLC using MEKA, which is a multi-label extension of the well-known Java library WEKA. The resulting approach recursively refines MEKA's multi-label classifiers, nesting other multi-label classifiers for meta algorithms and single-label classifiers provided by WEKA as base learners. In our evaluation, we find that the proposed approach yields strong results and performs significantly better than a set of baselines we compare with."}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2019-06-11T21:33:06Z","has_accepted_license":"1","status":"public","file":[{"file_size":388191,"file_id":"13177","creator":"wever","date_updated":"2019-09-10T08:20:44Z","content_type":"application/pdf","relation":"main_file","file_name":"Automating_MultiLabel_Classification_Extending_ML-Plan.pdf","date_created":"2019-09-10T08:19:01Z","access_level":"open_access"}],"department":[{"_id":"355"}],"file_date_updated":"2019-09-10T08:20:44Z","author":[{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"full_name":"Tornede, Alexander","first_name":"Alexander","id":"38209","last_name":"Tornede"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"oa":"1","conference":{"start_date":"2019-06-09","name":"6th ICML Workshop on Automated Machine Learning (AutoML 2019)","location":"Long Beach, CA, USA","end_date":"2019-06-15"},"_id":"10232","date_updated":"2022-01-06T06:50:33Z","language":[{"iso":"eng"}],"type":"conference","citation":{"mla":"Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending ML-Plan. 2019.","bibtex":"@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }","apa":"Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.","ama":"Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification Extending ML-Plan. In: ; 2019.","chicago":"Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier. “Automating Multi-Label Classification Extending ML-Plan,” 2019.","ieee":"M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019.","short":"M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019."},"year":"2019"},{"author":[{"first_name":"Katharina","full_name":"Rohlfing, Katharina","last_name":"Rohlfing","id":"50352"},{"full_name":"Leonardi, Giuseppe","first_name":"Giuseppe","last_name":"Leonardi"},{"first_name":"Iris","full_name":"Nomikou, Iris","last_name":"Nomikou"},{"last_name":"Rączaszek-Leonardi","full_name":"Rączaszek-Leonardi, Joanna","first_name":"Joanna"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"department":[{"_id":"749"},{"_id":"355"}],"publication":"IEEE Transactions on Cognitive and Developmental Systems","status":"public","date_created":"2020-11-02T13:25:49Z","title":"Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches","user_id":"14931","citation":{"short":"K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier, IEEE Transactions on Cognitive and Developmental Systems (2019).","ieee":"K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier, “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi: 10.1109/TCDS.2019.2892991.","chicago":"Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rączaszek-Leonardi, and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems, 2019. https://doi.org/10.1109/TCDS.2019.2892991.","apa":"Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., & Hüllermeier, E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. https://doi.org/10.1109/TCDS.2019.2892991","ama":"Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems. Published online 2019. doi:10.1109/TCDS.2019.2892991","mla":"Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems, 2019, doi:10.1109/TCDS.2019.2892991.","bibtex":"@article{Rohlfing_Leonardi_Nomikou_Rączaszek-Leonardi_Hüllermeier_2019, title={Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches}, DOI={10.1109/TCDS.2019.2892991}, journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing, Katharina and Leonardi, Giuseppe and Nomikou, Iris and Rączaszek-Leonardi, Joanna and Hüllermeier, Eyke}, year={2019} }"},"type":"journal_article","year":"2019","language":[{"iso":"eng"}],"date_updated":"2023-02-01T12:39:19Z","_id":"20243","doi":"10.1109/TCDS.2019.2892991"},{"ddc":["000"],"user_id":"49109","date_created":"2018-04-24T08:34:52Z","status":"public","has_accepted_license":"1","publication":"SCC","file_date_updated":"2018-11-06T15:08:39Z","publisher":"IEEE","author":[{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"},{"last_name":"Faez","first_name":"Amin","full_name":"Faez, Amin"}],"file":[{"file_size":237890,"file_id":"5382","creator":"wever","date_updated":"2018-11-06T15:08:39Z","content_type":"application/pdf","relation":"main_file","date_created":"2018-11-06T15:08:39Z","file_name":"08456425.pdf","access_level":"closed"}],"conference":{"end_date":"2018-07-07","start_date":"2018-07-02","name":"IEEE International Conference on Services Computing, SCC 2018","location":"San Francisco, CA, USA"},"_id":"2479","year":"2018","type":"conference","citation":{"ieee":"F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA, 2018.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco, CA, USA, 2018.","mla":"Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning Services.” SCC, IEEE, 2018, doi:10.1109/SCC.2018.00039.","bibtex":"@inproceedings{Mohr_Wever_Hüllermeier_Faez_2018, place={San Francisco, CA, USA}, title={(WIP) Towards the Automated Composition of Machine Learning Services}, DOI={10.1109/SCC.2018.00039}, booktitle={SCC}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke and Faez, Amin}, year={2018} }","chicago":"Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP) Towards the Automated Composition of Machine Learning Services.” In SCC. San Francisco, CA, USA: IEEE, 2018. https://doi.org/10.1109/SCC.2018.00039.","ama":"Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018. doi:10.1109/SCC.2018.00039","apa":"Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In SCC. San Francisco, CA, USA: IEEE. https://doi.org/10.1109/SCC.2018.00039"},"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/8456425","open_access":"1"}],"title":"(WIP) Towards the Automated Composition of Machine Learning Services","place":"San Francisco, CA, USA","publication_status":"published","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"department":[{"_id":"355"}],"doi":"10.1109/SCC.2018.00039","oa":"1","date_updated":"2022-01-06T06:56:35Z","language":[{"iso":"eng"}]},{"user_id":"13472","title":"Deep Architectures for Learning Context-dependent Ranking Functions","abstract":[{"text":"Object ranking is an important problem in the realm of preference learning.\r\nOn the basis of training data in the form of a set of rankings of objects,\r\nwhich are typically represented as feature vectors, the goal is to learn a\r\nranking function that predicts a linear order of any new set of objects.\r\nCurrent approaches commonly focus on ranking by scoring, i.e., on learning an\r\nunderlying latent utility function that seeks to capture the inherent utility\r\nof each object. These approaches, however, are not able to take possible\r\neffects of context-dependence into account, where context-dependence means that\r\nthe utility or usefulness of an object may also depend on what other objects\r\nare available as alternatives. In this paper, we formalize the problem of\r\ncontext-dependent ranking and present two general approaches based on two\r\nnatural representations of context-dependent ranking functions. Both approaches\r\nare instantiated by means of appropriate neural network architectures, which\r\nare evaluated on suitable benchmark task.","lang":"eng"}],"date_created":"2020-09-17T10:53:39Z","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"status":"public","department":[{"_id":"7"},{"_id":"355"}],"publication":"arXiv:1803.05796","author":[{"last_name":"Pfannschmidt","full_name":"Pfannschmidt, Karlson","first_name":"Karlson"},{"last_name":"Gupta","first_name":"Pritha","full_name":"Gupta, Pritha"},{"last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"_id":"19524","date_updated":"2022-01-06T06:54:06Z","language":[{"iso":"eng"}],"type":"preprint","year":"2018","citation":{"ieee":"K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Deep Architectures for Learning Context-dependent Ranking Functions,” arXiv:1803.05796. 2018.","short":"K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018).","mla":"Pfannschmidt, Karlson, et al. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018.","bibtex":"@article{Pfannschmidt_Gupta_Hüllermeier_2018, title={Deep Architectures for Learning Context-dependent Ranking Functions}, journal={arXiv:1803.05796}, author={Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke}, year={2018} }","ama":"Pfannschmidt K, Gupta P, Hüllermeier E. Deep Architectures for Learning Context-dependent Ranking Functions. arXiv:180305796. 2018.","apa":"Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2018). Deep Architectures for Learning Context-dependent Ranking Functions. ArXiv:1803.05796.","chicago":"Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018."}},{"page":"31-39","year":"2018","citation":{"chicago":"Mohr, Felix, Theodor Lettmann, Eyke Hüllermeier, and Marcel Dominik Wever. “Programmatic Task Network Planning.” In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, 31–39. AAAI, 2018.","apa":"Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical Planning (pp. 31–39). Delft, Netherlands: AAAI.","ama":"Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning. In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI; 2018:31-39.","bibtex":"@inproceedings{Mohr_Lettmann_Hüllermeier_Wever_2018, title={Programmatic Task Network Planning}, booktitle={Proceedings of the 1st ICAPS Workshop on Hierarchical Planning}, publisher={AAAI}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke and Wever, Marcel Dominik}, year={2018}, pages={31–39} }","mla":"Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.","short":"F. Mohr, T. Lettmann, E. Hüllermeier, M.D. Wever, in: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.","ieee":"F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task Network Planning,” in Proceedings of the 1st ICAPS Workshop on Hierarchical Planning, Delft, Netherlands, 2018, pp. 31–39."},"type":"conference","main_file_link":[{"open_access":"1","url":"http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf"}],"conference":{"start_date":"2018-06-24","name":"28th International Conference on Automated Planning and Scheduling","location":"Delft, Netherlands","end_date":"2018-06-29"},"_id":"2857","date_created":"2018-05-24T09:00:20Z","has_accepted_license":"1","status":"public","publication":"Proceedings of the 1st ICAPS Workshop on Hierarchical Planning","file_date_updated":"2018-11-06T15:18:26Z","author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"orcid":"0000-0001-5859-2457","full_name":"Lettmann, Theodor","first_name":"Theodor","id":"315","last_name":"Lettmann"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"}],"publisher":"AAAI","file":[{"date_created":"2018-11-06T15:18:26Z","file_name":"Mohr18ProgrammaticPlanning.pdf","access_level":"closed","file_id":"5384","creator":"wever","file_size":349958,"success":1,"relation":"main_file","content_type":"application/pdf","date_updated":"2018-11-06T15:18:26Z"}],"ddc":["000"],"user_id":"315","language":[{"iso":"eng"}],"oa":"1","date_updated":"2022-01-06T06:58:08Z","project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"department":[{"_id":"355"}],"title":"Programmatic Task Network Planning"},{"intvolume":" 64","_id":"24150","date_updated":"2022-01-06T06:56:08Z","issue":"6","year":"2018","type":"journal_article","citation":{"short":"A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620.","ieee":"A. Ramaswamy and S. Bhatnagar, “Stability of stochastic approximations with ‘controlled markov’ noise and temporal difference learning,” IEEE Transactions on Automatic Control, vol. 64, no. 6, pp. 2614–2620, 2018.","apa":"Ramaswamy, A., & Bhatnagar, S. (2018). Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control, 64(6), 2614–2620.","ama":"Ramaswamy A, Bhatnagar S. Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. IEEE Transactions on Automatic Control. 2018;64(6):2614-2620.","chicago":"Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stability of Stochastic Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning.” IEEE Transactions on Automatic Control 64, no. 6 (2018): 2614–20.","mla":"Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stability of Stochastic Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning.” IEEE Transactions on Automatic Control, vol. 64, no. 6, IEEE, 2018, pp. 2614–20.","bibtex":"@article{Ramaswamy_Bhatnagar_2018, title={Stability of stochastic approximations with “controlled markov” noise and temporal difference learning}, volume={64}, number={6}, journal={IEEE Transactions on Automatic Control}, publisher={IEEE}, author={Ramaswamy, Arunselvan and Bhatnagar, Shalabh}, year={2018}, pages={2614–2620} }"},"page":"2614-2620","language":[{"iso":"eng"}],"title":"Stability of stochastic approximations with “controlled markov” noise and temporal difference learning","user_id":"66937","author":[{"last_name":"Ramaswamy","id":"66937","first_name":"Arunselvan","full_name":"Ramaswamy, Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111"},{"first_name":"Shalabh","full_name":"Bhatnagar, Shalabh","last_name":"Bhatnagar"}],"publisher":"IEEE","publication":"IEEE Transactions on Automatic Control","department":[{"_id":"355"}],"volume":64,"status":"public","date_created":"2021-09-10T10:17:54Z"},{"language":[{"iso":"eng"}],"page":"737-742","citation":{"short":"B. Demirel, A. Ramaswamy, D.E. Quevedo, H. Karl, IEEE Control Systems Letters 2 (2018) 737–742.","ieee":"B. Demirel, A. Ramaswamy, D. E. Quevedo, and H. Karl, “Deepcas: A deep reinforcement learning algorithm for control-aware scheduling,” IEEE Control Systems Letters, vol. 2, no. 4, pp. 737–742, 2018.","ama":"Demirel B, Ramaswamy A, Quevedo DE, Karl H. Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters. 2018;2(4):737-742.","apa":"Demirel, B., Ramaswamy, A., Quevedo, D. E., & Karl, H. (2018). Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. IEEE Control Systems Letters, 2(4), 737–742.","chicago":"Demirel, Burak, Arunselvan Ramaswamy, Daniel E Quevedo, and Holger Karl. “Deepcas: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling.” IEEE Control Systems Letters 2, no. 4 (2018): 737–42.","bibtex":"@article{Demirel_Ramaswamy_Quevedo_Karl_2018, title={Deepcas: A deep reinforcement learning algorithm for control-aware scheduling}, volume={2}, number={4}, journal={IEEE Control Systems Letters}, publisher={IEEE}, author={Demirel, Burak and Ramaswamy, Arunselvan and Quevedo, Daniel E and Karl, Holger}, year={2018}, pages={737–742} }","mla":"Demirel, Burak, et al. “Deepcas: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling.” IEEE Control Systems Letters, vol. 2, no. 4, IEEE, 2018, pp. 737–42."},"year":"2018","type":"journal_article","intvolume":" 2","_id":"24151","date_updated":"2022-01-06T06:56:08Z","issue":"4","publication":"IEEE Control Systems Letters","department":[{"_id":"355"}],"author":[{"last_name":"Demirel","full_name":"Demirel, Burak","first_name":"Burak"},{"first_name":"Arunselvan","full_name":"Ramaswamy, Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111","last_name":"Ramaswamy","id":"66937"},{"first_name":"Daniel E","full_name":"Quevedo, Daniel E","last_name":"Quevedo"},{"last_name":"Karl","first_name":"Holger","full_name":"Karl, Holger"}],"publisher":"IEEE","date_created":"2021-09-10T10:19:07Z","status":"public","volume":2,"user_id":"66937","title":"Deepcas: A deep reinforcement learning algorithm for control-aware scheduling"},{"date_updated":"2022-01-06T06:56:32Z","oa":"1","doi":"10.1109/SCC.2018.00036","language":[{"iso":"eng"}],"place":"San Francisco, CA, USA","title":"On-The-Fly Service Construction with Prototypes","department":[{"_id":"355"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"_id":"2471","conference":{"end_date":"2018-07-07","location":"San Francisco, CA, USA","start_date":"2018-07-02","name":"IEEE International Conference on Services Computing, SCC 2018"},"main_file_link":[{"url":"https://ieeexplore.ieee.org/abstract/document/8456422","open_access":"1"}],"type":"conference","year":"2018","citation":{"ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in SCC, San Francisco, CA, USA, 2018.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018.","mla":"Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.” SCC, IEEE Computer Society, 2018, doi:10.1109/SCC.2018.00036.","bibtex":"@inproceedings{Mohr_Wever_Hüllermeier_2018, place={San Francisco, CA, USA}, title={On-The-Fly Service Construction with Prototypes}, DOI={10.1109/SCC.2018.00036}, booktitle={SCC}, publisher={IEEE Computer Society}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018} }","apa":"Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society. https://doi.org/10.1109/SCC.2018.00036","ama":"Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “On-The-Fly Service Construction with Prototypes.” In SCC. San Francisco, CA, USA: IEEE Computer Society, 2018. https://doi.org/10.1109/SCC.2018.00036."},"user_id":"49109","ddc":["000"],"file":[{"creator":"wever","file_id":"5383","file_size":356132,"success":1,"relation":"main_file","content_type":"application/pdf","date_updated":"2018-11-06T15:15:38Z","date_created":"2018-11-06T15:15:38Z","file_name":"08456422.pdf","access_level":"closed"}],"publisher":"IEEE Computer Society","author":[{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"last_name":"Wever","id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"file_date_updated":"2018-11-06T15:15:38Z","publication":"SCC","has_accepted_license":"1","status":"public","date_created":"2018-04-23T11:40:20Z"},{"_id":"3402","type":"journal_article","year":"2018","citation":{"bibtex":"@article{Melnikov_Hüllermeier_2018, title={On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis}, DOI={10.1007/s10994-018-5733-1}, journal={Machine Learning}, author={Melnikov, Vitalik and Hüllermeier, Eyke}, year={2018} }","mla":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning, 2018, doi:10.1007/s10994-018-5733-1.","chicago":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning, 2018. https://doi.org/10.1007/s10994-018-5733-1.","apa":"Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. https://doi.org/10.1007/s10994-018-5733-1","ama":"Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. Machine Learning. 2018. doi:10.1007/s10994-018-5733-1","ieee":"V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” Machine Learning, 2018.","short":"V. Melnikov, E. Hüllermeier, Machine Learning (2018)."},"abstract":[{"text":"In machine learning, so-called nested dichotomies are utilized as a reduction technique, i.e., to decompose a multi-class classification problem into a set of binary problems, which are solved using a simple binary classifier as a base learner. The performance of the (multi-class) classifier thus produced strongly depends on the structure of the decomposition. In this paper, we conduct an empirical study, in which we compare existing heuristics for selecting a suitable structure in the form of a nested dichotomy. Moreover, we propose two additional heuristics as natural completions. One of them is the Best-of-K heuristic, which picks the (presumably) best among K randomly generated nested dichotomies. Surprisingly, and in spite of its simplicity, it turns out to outperform the state of the art.","lang":"eng"}],"user_id":"15504","ddc":["000"],"file":[{"creator":"ups","file_id":"5305","file_size":1482882,"relation":"main_file","success":1,"date_updated":"2018-11-02T15:30:57Z","content_type":"application/pdf","date_created":"2018-11-02T15:30:57Z","file_name":"OnTheEffectivenessOfHeuristics.pdf","access_level":"closed"}],"publication":"Machine Learning","file_date_updated":"2018-11-02T15:30:57Z","author":[{"full_name":"Melnikov, Vitalik","first_name":"Vitalik","last_name":"Melnikov"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"date_created":"2018-06-29T07:44:26Z","has_accepted_license":"1","status":"public","date_updated":"2022-01-06T06:59:14Z","doi":"10.1007/s10994-018-5733-1","language":[{"iso":"eng"}],"title":"On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis","department":[{"_id":"355"}],"project":[{"name":"SFB 901 - Subproject B3","_id":"11"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"1","name":"SFB 901"}],"publication_identifier":{"issn":["1573-0565"]}},{"language":[{"iso":"eng"}],"oa":"1","doi":"10.1007/s10994-018-5735-z","date_updated":"2022-01-06T06:59:21Z","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"publication_identifier":{"eissn":["1573-0565"],"issn":["0885-6125"]},"publication_status":"epub_ahead","department":[{"_id":"355"},{"_id":"34"},{"_id":"7"},{"_id":"26"}],"title":"ML-Plan: Automated Machine Learning via Hierarchical Planning","type":"journal_article","year":"2018","citation":{"short":"F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,” Machine Learning, pp. 1495–1515, 2018, doi: 10.1007/s10994-018-5735-z.","ama":"Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z","apa":"Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. 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Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches."}]},{"_id":"3552","conference":{"location":"‘s-Hertogenbosch, the Netherlands","start_date":"2018-10-24","name":"Symposium on Intelligent Data Analysis","end_date":"2018-10-26"},"main_file_link":[{"url":"https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19","open_access":"1"}],"type":"conference","citation":{"bibtex":"@inproceedings{Mohr_Wever_Hüllermeier, place={‘s-Hertogenbosch, the Netherlands}, title={Reduction Stumps for Multi-Class Classification}, DOI={10.1007/978-3-030-01768-2_19}, booktitle={Proceedings of the Symposium on Intelligent Data Analysis}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke} }","mla":"Mohr, Felix, et al. “Reduction Stumps for Multi-Class Classification.” Proceedings of the Symposium on Intelligent Data Analysis, doi:10.1007/978-3-030-01768-2_19.","ama":"Mohr F, Wever MD, Hüllermeier E. 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For example, by recursively bisecting the original set of classes, so-called nested dichotomies define a set of binary classification problems that are organized in the structure of a binary tree. In contrast to the existing one-shot heuristics for constructing nested dichotomies and motivated by recent work on algorithm configuration, we propose a genetic algorithm for optimizing the structure of such dichotomies. A key component of this approach is the proposed genetic representation that facilitates the application of standard genetic operators, while still supporting the exchange of partial solutions under recombination. We evaluate the approach in an extensive experimental study, showing that it yields classifiers with superior generalization performance.","lang":"eng"}],"has_accepted_license":"1","status":"public","date_created":"2018-03-31T13:51:23Z","file":[{"file_name":"p561-wever.pdf","date_created":"2018-11-02T14:33:54Z","access_level":"closed","file_size":875404,"file_id":"5275","creator":"ups","content_type":"application/pdf","date_updated":"2018-11-02T14:33:54Z","relation":"main_file","success":1}],"publisher":"ACM","author":[{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"},{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018","file_date_updated":"2018-11-02T14:33:54Z","keyword":["Classification","Hierarchical Decomposition","Indirect Encoding"],"_id":"2109","conference":{"end_date":"2018-07-19","location":"Kyoto, Japan","start_date":"2018-07-15","name":"GECCO 2018"},"year":"2018","type":"conference","citation":{"ieee":"M. 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The described setting is widely known as opposed preferences in quality of the product and also applies to the context of service-oriented computing. In general, service-oriented computing emphasizes the construction of large software systems out of existing services, where services are small and self-contained pieces of software that adhere to a specified interface. Several implementations of the same interface are considered as several instances of the same service. Thereby, customers are interested in buying the best service implementation for their service composition wrt. to metrics, such as costs, energy, memory consumption, or execution time. One way to ensure the service quality is to employ certificates, which can come in different kinds: Technical certificates proving correctness can be automatically constructed by the service provider and again be automatically checked by the user. Digital certificates allow proof of the integrity of a product. Other certificates might be rolled out if service providers follow a good software construction principle, which is checked in annual audits. Whereas all of these certificates are handled differently in service markets, what they have in common is that they influence the buying decisions of customers. In this paper, we review state-of-the-art developments in certification with respect to service-oriented computing. 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Seemann, M.-L. Merten, M. Geierhos, D. Tophinke, E. Hüllermeier, in: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Association for Computational Linguistics (ACL), Stroudsburg, PA, USA, 2017, pp. 40–45.","ieee":"N. Seemann, M.-L. Merten, M. Geierhos, D. Tophinke, and E. Hüllermeier, “Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German,” in Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Vancouver, BC, Canada, 2017, pp. 40–45.","ama":"Seemann N, Merten M-L, Geierhos M, Tophinke D, Hüllermeier E. Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. In: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. 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Stroudsburg, PA, USA: Association for Computational Linguistics (ACL), 2017. https://doi.org/10.18653/v1/W17-2206.","mla":"Seemann, Nina, et al. “Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German.” Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Association for Computational Linguistics (ACL), 2017, pp. 40–45, doi:10.18653/v1/W17-2206.","bibtex":"@inproceedings{Seemann_Merten_Geierhos_Tophinke_Hüllermeier_2017, place={Stroudsburg, PA, USA}, title={Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German}, DOI={10.18653/v1/W17-2206}, booktitle={Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature}, publisher={Association for Computational Linguistics (ACL)}, author={Seemann, Nina and Merten, Marie-Luis and Geierhos, Michaela and Tophinke, Doris and Hüllermeier, Eyke}, year={2017}, pages={40–45} }"},"year":"2017","conference":{"end_date":"2017-08-04","location":"Vancouver, BC, Canada","start_date":"2017-07-31","name":"Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2017)"},"_id":"1158","date_created":"2018-01-31T15:32:33Z","status":"public","publication":"Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature","publisher":"Association for Computational Linguistics (ACL)","author":[{"full_name":"Seemann, Nina","first_name":"Nina","id":"65408","last_name":"Seemann"},{"full_name":"Merten, Marie-Luis","first_name":"Marie-Luis","last_name":"Merten"},{"id":"42496","last_name":"Geierhos","orcid":"0000-0002-8180-5606","full_name":"Geierhos, Michaela","first_name":"Michaela"},{"last_name":"Tophinke","first_name":"Doris","full_name":"Tophinke, Doris"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"quality_controlled":"1","user_id":"13929","abstract":[{"text":"In this paper, we present the annotation challenges we have encountered when working on a historical language that was undergoing elaboration processes. We especially focus on syntactic ambiguity and gradience in Middle Low German, which causes uncertainty to some extent. Since current annotation tools consider construction contexts and the dynamics of the grammaticalization only partially, we plan to extend CorA – a web-based annotation tool for historical and other non-standard language data – to capture elaboration phenomena and annotator unsureness. Moreover, we seek to interactively learn morphological as well as syntactic annotations.","lang":"eng"}],"language":[{"iso":"eng"}],"doi":"10.18653/v1/W17-2206","date_updated":"2022-01-06T06:51:03Z","project":[{"name":"InterGramm","_id":"39"}],"publication_status":"published","department":[{"_id":"36"},{"_id":"579"},{"_id":"115"},{"_id":"355"},{"_id":"615"}],"title":"Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German","place":"Stroudsburg, PA, USA"},{"year":"2017","type":"bachelorsthesis","citation":{"short":"N.N. 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Solving the Container Pre-Marshalling Problem Using Reinforcement Learning and Structured Output Prediction. Universität Paderborn, 2017.","bibtex":"@book{Hetzer_Tornede_2017, title={Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction}, publisher={Universität Paderborn}, author={Hetzer, Alexander and Tornede, Tanja}, year={2017} }","chicago":"Hetzer, Alexander, and Tanja Tornede. Solving the Container Pre-Marshalling Problem Using Reinforcement Learning and Structured Output Prediction. Universität Paderborn, 2017.","apa":"Hetzer, A., & Tornede, T. (2017). Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction. Universität Paderborn.","ama":"Hetzer A, Tornede T. Solving the Container Pre-Marshalling Problem Using Reinforcement Learning and Structured Output Prediction. Universität Paderborn; 2017.","ieee":"A. Hetzer and T. Tornede, Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction. Universität Paderborn, 2017.","short":"A. Hetzer, T. Tornede, Solving the Container Pre-Marshalling Problem Using Reinforcement Learning and Structured Output Prediction, Universität Paderborn, 2017."},"type":"mastersthesis"},{"project":[{"name":"SFB 901","_id":"1"},{"_id":"12","name":"SFB 901 - Subprojekt B4"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B3","_id":"11"}],"department":[{"_id":"355"},{"_id":"77"}],"title":"Predicting Rankings of Software Verification Tools","language":[{"iso":"eng"}],"series_title":"SWAN'17","doi":"10.1145/3121257.3121262","date_updated":"2022-01-06T07:03:28Z","date_created":"2017-10-17T12:41:05Z","status":"public","has_accepted_license":"1","file":[{"file_name":"fsews17swan-swanmain1.pdf","date_created":"2018-11-02T14:24:29Z","access_level":"closed","file_id":"5271","creator":"ups","file_size":822383,"success":1,"relation":"main_file","content_type":"application/pdf","date_updated":"2018-11-02T14:24:29Z"}],"publication":"Proceedings of the 3rd International Workshop on Software Analytics","file_date_updated":"2018-11-02T14:24:29Z","author":[{"first_name":"Mike","full_name":"Czech, Mike","last_name":"Czech"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"},{"last_name":"Jakobs","full_name":"Jakobs, Marie-Christine","first_name":"Marie-Christine"},{"first_name":"Heike","full_name":"Wehrheim, Heike","last_name":"Wehrheim","id":"573"}],"user_id":"15504","ddc":["000"],"abstract":[{"text":"Today, software verification tools have reached the maturity to be used for large scale programs. Different tools perform differently well on varying code. A software developer is hence faced with the problem of choosing a tool appropriate for her program at hand. A ranking of tools on programs could facilitate the choice. Such rankings can, however, so far only be obtained by running all considered tools on the program.In this paper, we present a machine learning approach to predicting rankings of tools on programs. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for programs. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from the software verification competition SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy (rank correlation > 0.6).","lang":"eng"}],"page":"23-26","citation":{"bibtex":"@inproceedings{Czech_Hüllermeier_Jakobs_Wehrheim_2017, series={SWAN’17}, title={Predicting Rankings of Software Verification Tools}, DOI={10.1145/3121257.3121262}, booktitle={Proceedings of the 3rd International Workshop on Software Analytics}, author={Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}, year={2017}, pages={23–26}, collection={SWAN’17} }","mla":"Czech, Mike, et al. “Predicting Rankings of Software Verification Tools.” Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26, doi:10.1145/3121257.3121262.","chicago":"Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. “Predicting Rankings of Software Verification Tools.” In Proceedings of the 3rd International Workshop on Software Analytics, 23–26. SWAN’17, 2017. https://doi.org/10.1145/3121257.3121262.","apa":"Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Tools. In Proceedings of the 3rd International Workshop on Software Analytics (pp. 23–26). https://doi.org/10.1145/3121257.3121262","ama":"Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Tools. In: Proceedings of the 3rd International Workshop on Software Analytics. SWAN’17. ; 2017:23-26. doi:10.1145/3121257.3121262","ieee":"M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting Rankings of Software Verification Tools,” in Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.","short":"M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26."},"year":"2017","type":"conference","_id":"71"},{"language":[{"iso":"eng"}],"year":"2017","citation":{"apa":"Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Competitions.","ama":"Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Competitions.; 2017.","chicago":"Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim. Predicting Rankings of Software Verification Competitions, 2017.","bibtex":"@book{Czech_Hüllermeier_Jakobs_Wehrheim_2017, title={Predicting Rankings of Software Verification Competitions}, author={Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim, Heike}, year={2017} }","mla":"Czech, Mike, et al. Predicting Rankings of Software Verification Competitions. 2017.","short":"M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Predicting Rankings of Software Verification Competitions, 2017.","ieee":"M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, Predicting Rankings of Software Verification Competitions. 2017."},"type":"report","_id":"72","date_updated":"2022-01-06T07:03:29Z","status":"public","has_accepted_license":"1","date_created":"2017-10-17T12:41:05Z","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Subprojekt B3","_id":"11"},{"_id":"12","name":"SFB 901 - Subprojekt B4"},{"name":"SFB 901 - Project Area B","_id":"3"}],"file":[{"date_created":"2018-11-21T10:50:11Z","file_name":"Predicting Rankings of Soware Verification Competitions.pdf","access_level":"closed","file_id":"5782","creator":"florida","file_size":869984,"success":1,"relation":"main_file","content_type":"application/pdf","date_updated":"2018-11-21T10:50:11Z"}],"author":[{"last_name":"Czech","full_name":"Czech, Mike","first_name":"Mike"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"last_name":"Jakobs","full_name":"Jakobs, Marie-Christine","first_name":"Marie-Christine"},{"full_name":"Wehrheim, Heike","first_name":"Heike","id":"573","last_name":"Wehrheim"}],"file_date_updated":"2018-11-21T10:50:11Z","department":[{"_id":"77"},{"_id":"355"}],"user_id":"15504","ddc":["000"],"title":"Predicting Rankings of Software Verification Competitions","abstract":[{"text":"Software verification competitions, such as the annual SV-COMP, evaluate software verification tools with respect to their effectivity and efficiency. Typically, the outcome of a competition is a (possibly category-specific) ranking of the tools. For many applications, such as building portfolio solvers, it would be desirable to have an idea of the (relative) performance of verification tools on a given verification task beforehand, i.e., prior to actually running all tools on the task.In this paper, we present a machine learning approach to predicting rankings of tools on verification tasks. The method builds upon so-called label ranking algorithms, which we complement with appropriate kernels providing a similarity measure for verification tasks. Our kernels employ a graph representation for software source code that mixes elements of control flow and program dependence graphs with abstract syntax trees. Using data sets from SV-COMP, we demonstrate our rank prediction technique to generalize well and achieve a rather high predictive accuracy. In particular, our method outperforms a recently proposed feature-based approach of Demyanova et al. (when applied to rank predictions). ","lang":"eng"}]},{"status":"public","date_created":"2019-07-09T15:37:09Z","author":[{"last_name":"Fürnkranz","full_name":"Fürnkranz, J.","first_name":"J."},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"publication":"Encyclopedia of Machine Learning and Data Mining","user_id":"49109","title":"Preference Learning","language":[{"iso":"eng"}],"citation":{"short":"J. Fürnkranz, E. Hüllermeier, in: Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–1005.","ieee":"J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–1005.","ama":"Fürnkranz J, Hüllermeier E. Preference Learning. In: Encyclopedia of Machine Learning and Data Mining. ; 2017:1000-1005.","apa":"Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In Encyclopedia of Machine Learning and Data Mining (pp. 1000–1005).","chicago":"Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia of Machine Learning and Data Mining, 1000–1005, 2017.","mla":"Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–05.","bibtex":"@inbook{Fürnkranz_Hüllermeier_2017, title={Preference Learning}, booktitle={Encyclopedia of Machine Learning and Data Mining}, author={Fürnkranz, J. and Hüllermeier, Eyke}, year={2017}, pages={1000–1005} }"},"type":"encyclopedia_article","year":"2017","page":"1000-1005","date_updated":"2022-01-06T06:50:45Z","_id":"10589"},{"year":"2017","citation":{"chicago":"Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, 107:1000–1005. Springer, 2017.","apa":"Fürnkranz, J., & Hüllermeier, E. (2017). Preference Learning. In C. Sammut & G. I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining (Vol. 107, pp. 1000–1005). Springer.","ama":"Fürnkranz J, Hüllermeier E. Preference Learning. In: Sammut C, Webb GI, eds. Encyclopedia of Machine Learning and Data Mining. Vol 107. Springer; 2017:1000-1005.","mla":"Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, edited by C. Sammut and G.I. Webb, vol. 107, Springer, 2017, pp. 1000–05.","bibtex":"@inbook{Fürnkranz_Hüllermeier_2017, title={Preference Learning}, volume={107}, booktitle={Encyclopedia of Machine Learning and Data Mining}, publisher={Springer}, author={Fürnkranz, J. and Hüllermeier, Eyke}, editor={Sammut, C. and Webb, G.I.Editors}, year={2017}, pages={1000–1005} }","short":"J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2017, pp. 1000–1005.","ieee":"J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, vol. 107, C. Sammut and G. I. Webb, Eds. Springer, 2017, pp. 1000–1005."},"type":"book_chapter","page":"1000-1005","language":[{"iso":"eng"}],"_id":"10784","date_updated":"2022-01-06T06:50:50Z","intvolume":" 107","publisher":"Springer","author":[{"full_name":"Fürnkranz, J.","first_name":"J.","last_name":"Fürnkranz"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"publication":"Encyclopedia of Machine Learning and Data Mining","editor":[{"full_name":"Sammut, C.","first_name":"C.","last_name":"Sammut"},{"last_name":"Webb","full_name":"Webb, G.I.","first_name":"G.I."}],"volume":107,"status":"public","date_created":"2019-07-10T15:44:32Z","title":"Preference Learning","user_id":"49109"},{"user_id":"49109","ddc":["000"],"abstract":[{"text":"These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings.","lang":"eng"}],"status":"public","has_accepted_license":"1","date_created":"2018-02-22T07:19:18Z","file":[{"date_created":"2018-11-06T15:28:09Z","file_name":"CI Workshop AutoML.pdf","access_level":"closed","file_id":"5387","creator":"wever","file_size":323589,"relation":"main_file","success":1,"content_type":"application/pdf","date_updated":"2018-11-06T15:28:09Z"}],"author":[{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"file_date_updated":"2018-11-06T15:28:09Z","publication":"27th Workshop Computational Intelligence","_id":"1180","conference":{"end_date":"2017-11-24","location":"Dortmund","name":"27th Workshop Computational Intelligence","start_date":"2017-11-23"},"year":"2017","type":"conference","citation":{"ieee":"M. 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In order to optimally satisfy service requesters and service providers, adequate techniques for automatic service matching are needed. However, a requester’s requirements may be vague and the information available about a provided service may be incomplete. As a consequence, fuzziness is induced into the matching procedure. The contribution of this paper is the development of a systematic matching procedure that leverages concepts and techniques from fuzzy logic and possibility theory based on our formal distinction between different sources and types of fuzziness in the context of service matching. In contrast to existing methods, our approach is able to deal with imprecision and incompleteness in service specifications and to inform users about the extent of induced fuzziness in order to improve the user’s decision-making. We demonstrate our approach on the example of specifications for service reputation based on ratings given by previous users. Our evaluation based on real service ratings shows the utility and applicability of our approach."}]},{"project":[{"_id":"1","name":"SFB 901"},{"_id":"11","name":"SFB 901 - Subprojekt B3"},{"_id":"3","name":"SFB 901 - Project Area B"}],"department":[{"_id":"355"}],"title":"Learning to Aggregate Using Uninorms","language":[{"iso":"eng"}],"series_title":"LNCS","doi":"10.1007/978-3-319-46227-1_47","date_updated":"2022-01-06T06:53:32Z","status":"public","has_accepted_license":"1","date_created":"2017-10-17T12:41:27Z","file":[{"file_size":472159,"creator":"florida","file_id":"1533","content_type":"application/pdf","date_updated":"2018-03-21T12:32:44Z","relation":"main_file","success":1,"date_created":"2018-03-21T12:32:44Z","file_name":"184-chp_3A10.1007_2F978-3-319-46227-1_47.pdf","access_level":"closed"}],"author":[{"full_name":"Melnikov, Vitaly","first_name":"Vitaly","id":"58747","last_name":"Melnikov"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publication":"Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016)","file_date_updated":"2018-03-21T12:32:44Z","user_id":"15504","ddc":["040"],"abstract":[{"text":"In this paper, we propose a framework for a class of learning problems that we refer to as “learning to aggregate”. Roughly, learning-to-aggregate problems are supervised machine learning problems, in which instances are represented in the form of a composition of a (variable) number on constituents; such compositions are associated with an evaluation, score, or label, which is the target of the prediction task, and which can presumably be modeled in the form of a suitable aggregation of the properties of its constituents. Our learning-to-aggregate framework establishes a close connection between machine learning and a branch of mathematics devoted to the systematic study of aggregation functions. We specifically focus on a class of functions called uninorms, which combine conjunctive and disjunctive modes of aggregation. Experimental results for a corresponding model are presented for a review data set, for which the aggregation problem consists of combining different reviewer opinions about a paper into an overall decision of acceptance or rejection.","lang":"eng"}],"year":"2016","type":"conference","citation":{"short":"V. Melnikov, E. Hüllermeier, in: Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–771.","ieee":"V. Melnikov and E. Hüllermeier, “Learning to Aggregate Using Uninorms,” in Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 2016, pp. 756–771.","chicago":"Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate Using Uninorms.” In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD 2016), 756–71. 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It proposes the paradigm of On-The-Fly Computing, which tackles emerging worldwide service markets. In these markets, service providers trade software, platform, and infrastructure as a service. Service requesters state requirements on services. To satisfy these requirements, the new role of brokers, who are (human) actors building service compositions on the fly, is introduced. Brokers have to specify service compositions formally and comprehensively using a domain-specific language (DSL), and to use service matching for the discovery of the constituent services available in the market. The broker's choice of the DSL and matching approaches influences her success of building compositions as distinctive properties of different service markets play a significant role. In this paper, we propose a new approach of engineering a situation-specific DSL by customizing a comprehensive, modular DSL and its matching for given service market properties. This enables the broker to create market-specific composition specifications and to perform market-specific service matching. As a result, the broker builds service compositions satisfying the requester's requirements more accurately. We evaluated the presented concepts using case studies in service markets for tourism and university management."}],"ddc":["040"],"user_id":"477"},{"page":"1-18","citation":{"short":"A. Jungmann, F. Mohr, Journal of Internet Services and Applications (2015) 1–18.","ieee":"A. Jungmann and F. Mohr, “An approach towards adaptive service composition in markets of composed services,” Journal of Internet Services and Applications, no. 1, pp. 1–18, 2015.","ama":"Jungmann A, Mohr F. An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications. 2015;(1):1-18. doi:10.1186/s13174-015-0022-8","apa":"Jungmann, A., & Mohr, F. (2015). An approach towards adaptive service composition in markets of composed services. Journal of Internet Services and Applications, (1), 1–18. https://doi.org/10.1186/s13174-015-0022-8","chicago":"Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service Composition in Markets of Composed Services.” Journal of Internet Services and Applications, no. 1 (2015): 1–18. https://doi.org/10.1186/s13174-015-0022-8.","mla":"Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service Composition in Markets of Composed Services.” Journal of Internet Services and Applications, no. 1, Springer, 2015, pp. 1–18, doi:10.1186/s13174-015-0022-8.","bibtex":"@article{Jungmann_Mohr_2015, title={An approach towards adaptive service composition in markets of composed services}, DOI={10.1186/s13174-015-0022-8}, number={1}, journal={Journal of Internet Services and Applications}, publisher={Springer}, author={Jungmann, Alexander and Mohr, Felix}, year={2015}, pages={1–18} }"},"year":"2015","type":"journal_article","_id":"323","issue":"1","file_date_updated":"2018-03-20T07:39:17Z","publication":"Journal of Internet Services and Applications","author":[{"full_name":"Jungmann, Alexander","first_name":"Alexander","last_name":"Jungmann"},{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"}],"publisher":"Springer","file":[{"success":1,"relation":"main_file","content_type":"application/pdf","date_updated":"2018-03-20T07:39:17Z","creator":"florida","file_id":"1429","file_size":2842281,"access_level":"closed","date_created":"2018-03-20T07:39:17Z","file_name":"323-An_approach_towards_adaptive_service_composition_in_markets_of_composed_services.pdf"}],"date_created":"2017-10-17T12:41:55Z","status":"public","has_accepted_license":"1","abstract":[{"lang":"eng","text":"On-the-fly composition of service-based software solutions is still a challenging task. Even more challenges emerge when facing automatic service composition in markets of composed services for end users. In this paper, we focus on the functional discrepancy between “what a user wants” specified in terms of a request and “what a user gets” when executing a composed service. To meet the challenge of functional discrepancy, we propose the combination of existing symbolic composition approaches with machine learning techniques. We developed a learning recommendation system that expands the capabilities of existing composition algorithms to facilitate adaptivity and consequently reduces functional discrepancy. As a representative of symbolic techniques, an Artificial Intelligence planning based approach produces solutions that are correct with respect to formal specifications. Our learning recommendation system supports the symbolic approach in decision-making. Reinforcement Learning techniques enable the recommendation system to adjust its recommendation strategy over time based on user ratings. We implemented the proposed functionality in terms of a prototypical composition framework. Preliminary results from experiments conducted in the image processing domain illustrate the benefit of combining both complementary techniques."}],"ddc":["040"],"user_id":"477","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:59:06Z","doi":"10.1186/s13174-015-0022-8","department":[{"_id":"355"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Subprojekt B2","_id":"10"},{"_id":"3","name":"SFB 901 - Project Area B"}],"title":"An approach towards adaptive service composition in markets of composed services"},{"year":"2015","citation":{"ieee":"F. Mohr, “A Metric for Functional Reusability of Services,” in Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313.","short":"F. Mohr, in: Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313.","bibtex":"@inproceedings{Mohr_2015, series={LNCS}, title={A Metric for Functional Reusability of Services}, DOI={10.1007/978-3-319-14130-5_21}, booktitle={Proceedings of the 14th International Conference on Software Reuse (ICSR)}, author={Mohr, Felix}, year={2015}, pages={298--313}, collection={LNCS} }","mla":"Mohr, Felix. “A Metric for Functional Reusability of Services.” Proceedings of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313, doi:10.1007/978-3-319-14130-5_21.","chicago":"Mohr, Felix. “A Metric for Functional Reusability of Services.” In Proceedings of the 14th International Conference on Software Reuse (ICSR), 298--313. LNCS, 2015. https://doi.org/10.1007/978-3-319-14130-5_21.","ama":"Mohr F. A Metric for Functional Reusability of Services. In: Proceedings of the 14th International Conference on Software Reuse (ICSR). LNCS. ; 2015:298--313. doi:10.1007/978-3-319-14130-5_21","apa":"Mohr, F. (2015). A Metric for Functional Reusability of Services. In Proceedings of the 14th International Conference on Software Reuse (ICSR) (pp. 298--313). https://doi.org/10.1007/978-3-319-14130-5_21"},"type":"conference","page":"298--313","_id":"324","author":[{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"}],"publication":"Proceedings of the 14th International Conference on Software Reuse (ICSR)","file_date_updated":"2018-03-20T07:38:44Z","file":[{"relation":"main_file","success":1,"date_updated":"2018-03-20T07:38:44Z","content_type":"application/pdf","file_id":"1428","creator":"florida","file_size":569475,"access_level":"closed","file_name":"324-ICSR-Mohr-15.pdf","date_created":"2018-03-20T07:38:44Z"}],"status":"public","has_accepted_license":"1","date_created":"2017-10-17T12:41:55Z","abstract":[{"lang":"eng","text":"Services are self-contained software components that can beused platform independent and that aim at maximizing software reuse. Abasic concern in service oriented architectures is to measure the reusabilityof services. One of the most important qualities is the functionalreusability, which indicates how relevant the task is that a service solves.Current metrics for functional reusability of software, however, have verylittle explanatory power and do not accomplish this goal.This paper presents a new approach to estimate the functional reusabilityof services based on their relevance. To this end, it denes the degreeto which a service enables the execution of other services as its contri-bution. Based on the contribution, relevance of services is dened as anestimation for their functional reusability. Explanatory power is obtainedby normalizing relevance values with a reference service. The applicationof the metric to a service test set conrms its supposed capabilities."}],"ddc":["040"],"user_id":"477","series_title":"LNCS","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:59:07Z","doi":"10.1007/978-3-319-14130-5_21","department":[{"_id":"355"}],"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Subprojekt B2","_id":"10"},{"name":"SFB 901 - Project Area B","_id":"3"}],"title":"A Metric for Functional Reusability of Services"},{"type":"conference","citation":{"chicago":"Mohr, Felix, Alexander Jungmann, and Hans Kleine Büning. “Automated Online Service Composition.” In Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 57--64, 2015. https://doi.org/10.1109/SCC.2015.18.","apa":"Mohr, F., Jungmann, A., & Kleine Büning, H. (2015). Automated Online Service Composition. In Proceedings of the 12th IEEE International Conference on Services Computing (SCC) (pp. 57--64). https://doi.org/10.1109/SCC.2015.18","ama":"Mohr F, Jungmann A, Kleine Büning H. Automated Online Service Composition. In: Proceedings of the 12th IEEE International Conference on Services Computing (SCC). ; 2015:57--64. doi:10.1109/SCC.2015.18","mla":"Mohr, Felix, et al. “Automated Online Service Composition.” Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64, doi:10.1109/SCC.2015.18.","bibtex":"@inproceedings{Mohr_Jungmann_Kleine Büning_2015, title={Automated Online Service Composition}, DOI={10.1109/SCC.2015.18}, booktitle={Proceedings of the 12th IEEE International Conference on Services Computing (SCC)}, author={Mohr, Felix and Jungmann, Alexander and Kleine Büning, Hans}, year={2015}, pages={57--64} }","short":"F. Mohr, A. Jungmann, H. Kleine Büning, in: Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64.","ieee":"F. Mohr, A. Jungmann, and H. Kleine Büning, “Automated Online Service Composition,” in Proceedings of the 12th IEEE International Conference on Services Computing (SCC), 2015, pp. 57--64."},"year":"2015","page":"57--64","_id":"319","author":[{"last_name":"Mohr","full_name":"Mohr, Felix","first_name":"Felix"},{"last_name":"Jungmann","full_name":"Jungmann, Alexander","first_name":"Alexander"},{"first_name":"Hans","full_name":"Kleine Büning, Hans","last_name":"Kleine Büning"}],"publication":"Proceedings of the 12th IEEE International Conference on Services Computing (SCC)","file_date_updated":"2018-03-20T07:42:03Z","file":[{"access_level":"closed","date_created":"2018-03-20T07:42:03Z","file_name":"319-07207336.pdf","date_updated":"2018-03-20T07:42:03Z","content_type":"application/pdf","success":1,"relation":"main_file","file_size":345742,"creator":"florida","file_id":"1434"}],"status":"public","has_accepted_license":"1","date_created":"2017-10-17T12:41:54Z","abstract":[{"text":"Services are self-contained and platform independent software components that aim at maximizing software reuse. The automated composition of services to a target software artifact has been tackled with many AI techniques, but existing approaches make unreasonably strong assumptions such as a predefined data flow, are limited to tiny problem sizes, ignore non-functional properties, or assume offline service repositories. This paper presents an algorithm that automatically composes services without making such assumptions. We employ a backward search algorithm that starts from an empty composition and prepends service calls to already discovered candidates until a solution is found. Available services are determined during the search process. We implemented our algorithm, performed an experimental evaluation, and compared it to other approaches.","lang":"eng"}],"ddc":["040"],"user_id":"477","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:59:04Z","doi":"10.1109/SCC.2015.18","department":[{"_id":"355"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Subprojekt B2","_id":"10"},{"_id":"3","name":"SFB 901 - Project Area B"}],"title":"Automated Online Service Composition"},{"project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B3","_id":"11"}],"publication_status":"published","publication_identifier":{"issn":["1063-6706","1941-0034"]},"department":[{"_id":"355"}],"title":"Fast Fuzzy Pattern Tree Learning for Classification","language":[{"iso":"eng"}],"doi":"10.1109/tfuzz.2015.2396078","date_updated":"2022-01-06T07:01:22Z","date_created":"2018-10-22T06:53:37Z","status":"public","has_accepted_license":"1","volume":23,"file":[{"relation":"main_file","success":1,"date_updated":"2018-11-02T15:53:23Z","content_type":"application/pdf","creator":"ups","file_id":"5316","file_size":732827,"access_level":"closed","file_name":"07018950.pdf","date_created":"2018-11-02T15:53:23Z"}],"publication":"IEEE Transactions on Fuzzy Systems","file_date_updated":"2018-11-02T15:53:23Z","publisher":"Institute of Electrical and Electronics Engineers (IEEE)","author":[{"full_name":"Senge, Robin","first_name":"Robin","last_name":"Senge"},{"last_name":"Hüllermeier","id":"48129","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"user_id":"49109","ddc":["000"],"page":"2024-2033","year":"2015","citation":{"ieee":"R. Senge and E. Hüllermeier, “Fast Fuzzy Pattern Tree Learning for Classification,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, pp. 2024–2033, 2015.","short":"R. Senge, E. Hüllermeier, IEEE Transactions on Fuzzy Systems 23 (2015) 2024–2033.","mla":"Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning for Classification.” IEEE Transactions on Fuzzy Systems, vol. 23, no. 6, Institute of Electrical and Electronics Engineers (IEEE), 2015, pp. 2024–33, doi:10.1109/tfuzz.2015.2396078.","bibtex":"@article{Senge_Hüllermeier_2015, title={Fast Fuzzy Pattern Tree Learning for Classification}, volume={23}, DOI={10.1109/tfuzz.2015.2396078}, number={6}, journal={IEEE Transactions on Fuzzy Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Senge, Robin and Hüllermeier, Eyke}, year={2015}, pages={2024–2033} }","chicago":"Senge, Robin, and Eyke Hüllermeier. “Fast Fuzzy Pattern Tree Learning for Classification.” IEEE Transactions on Fuzzy Systems 23, no. 6 (2015): 2024–33. https://doi.org/10.1109/tfuzz.2015.2396078.","apa":"Senge, R., & Hüllermeier, E. (2015). Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems, 23(6), 2024–2033. https://doi.org/10.1109/tfuzz.2015.2396078","ama":"Senge R, Hüllermeier E. Fast Fuzzy Pattern Tree Learning for Classification. IEEE Transactions on Fuzzy Systems. 2015;23(6):2024-2033. doi:10.1109/tfuzz.2015.2396078"},"type":"journal_article","issue":"6","intvolume":" 23","_id":"4792"},{"citation":{"ieee":"D. Schäfer and E. Hüllermeier, “Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations,” in in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal, 2015, pp. 110–111.","short":"D. Schäfer, E. Hüllermeier, in: In Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection Co-Located ECML/PKDD, Porto, Portugal, 2015, pp. 110–111.","mla":"Schäfer, D., and Eyke Hüllermeier. “Preference-Based Meta-Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations.” In Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection Co-Located ECML/PKDD, Porto, Portugal, 2015, pp. 110–11.","bibtex":"@inproceedings{Schäfer_Hüllermeier_2015, title={Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations}, booktitle={in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal}, author={Schäfer, D. and Hüllermeier, Eyke}, year={2015}, pages={110–111} }","ama":"Schäfer D, Hüllermeier E. Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations. In: In Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection Co-Located ECML/PKDD, Porto, Portugal. ; 2015:110-111.","apa":"Schäfer, D., & Hüllermeier, E. (2015). Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations. In in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal (pp. 110–111).","chicago":"Schäfer, D., and Eyke Hüllermeier. “Preference-Based Meta-Learning Using Dyad Ranking: Recommending Algorithms in Cold-Start Situations.” In In Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection Co-Located ECML/PKDD, Porto, Portugal, 110–11, 2015."},"year":"2015","type":"conference","page":"110-111","language":[{"iso":"eng"}],"_id":"15406","date_updated":"2022-01-06T06:52:23Z","status":"public","date_created":"2019-12-19T16:52:09Z","author":[{"last_name":"Schäfer","first_name":"D.","full_name":"Schäfer, D."},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"publication":"in Proceedings of the 2015 international Workshop on Meta-Learning and Algorithm Selection co-located ECML/PKDD, Porto, Portugal","title":"Preference-based meta-learning using dyad ranking: Recommending algorithms in cold-start situations","user_id":"49109"},{"publication":"In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany","department":[{"_id":"34"},{"_id":"7"},{"_id":"355"}],"author":[{"full_name":"Paul, Adil","first_name":"Adil","last_name":"Paul"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"date_created":"2020-02-03T14:07:45Z","status":"public","user_id":"49109","title":"A cbr approach to the angry birds game","language":[{"iso":"eng"}],"page":"68-77","citation":{"ieee":"A. Paul and E. Hüllermeier, “A cbr approach to the angry birds game,” in In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany, 2015, pp. 68–77.","short":"A. Paul, E. Hüllermeier, in: In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany, 2015, pp. 68–77.","bibtex":"@inproceedings{Paul_Hüllermeier_2015, title={A cbr approach to the angry birds game}, booktitle={In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany}, author={Paul, Adil and Hüllermeier, Eyke}, year={2015}, pages={68–77} }","mla":"Paul, Adil, and Eyke Hüllermeier. “A Cbr Approach to the Angry Birds Game.” In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany, 2015, pp. 68–77.","ama":"Paul A, Hüllermeier E. A cbr approach to the angry birds game. In: In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany. ; 2015:68-77.","apa":"Paul, A., & Hüllermeier, E. (2015). A cbr approach to the angry birds game. In In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany (pp. 68–77).","chicago":"Paul, Adil, and Eyke Hüllermeier. “A Cbr Approach to the Angry Birds Game.” In In Workshop Proceedings from ICCBR, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany, 68–77, 2015."},"type":"conference","year":"2015","_id":"15749","date_updated":"2022-01-06T06:52:32Z"}]