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San Francisco, CA, USA: IEEE. 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","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} }","mla":"Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning Services.” SCC, IEEE, 2018, doi:10.1109/SCC.2018.00039."},"type":"conference","year":"2018","ddc":["000"],"user_id":"49109","author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"id":"33176","last_name":"Wever","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik"},{"first_name":"Eyke","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","id":"48129"},{"first_name":"Amin","full_name":"Faez, Amin","last_name":"Faez"}],"publisher":"IEEE","file_date_updated":"2018-11-06T15:08:39Z","publication":"SCC","file":[{"content_type":"application/pdf","date_updated":"2018-11-06T15:08:39Z","relation":"main_file","file_size":237890,"creator":"wever","file_id":"5382","access_level":"closed","date_created":"2018-11-06T15:08:39Z","file_name":"08456425.pdf"}],"status":"public","has_accepted_license":"1","date_created":"2018-04-24T08:34:52Z","date_updated":"2022-01-06T06:56:35Z","doi":"10.1109/SCC.2018.00039","oa":"1","language":[{"iso":"eng"}],"place":"San Francisco, CA, USA","title":"(WIP) Towards the Automated Composition of Machine Learning Services","department":[{"_id":"355"}],"publication_status":"published","project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}]},{"citation":{"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} }","mla":"Pfannschmidt, Karlson, et al. “Deep Architectures for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 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.","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)."},"year":"2018","type":"preprint","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:54:06Z","_id":"19524","status":"public","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"date_created":"2020-09-17T10:53:39Z","author":[{"full_name":"Pfannschmidt, Karlson","first_name":"Karlson","last_name":"Pfannschmidt"},{"full_name":"Gupta, Pritha","first_name":"Pritha","last_name":"Gupta"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","last_name":"Hüllermeier"}],"publication":"arXiv:1803.05796","department":[{"_id":"7"},{"_id":"355"}],"title":"Deep Architectures for Learning Context-dependent Ranking Functions","user_id":"13472","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"}]},{"language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:58:08Z","oa":"1","department":[{"_id":"355"}],"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"title":"Programmatic Task Network Planning","main_file_link":[{"url":"http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf","open_access":"1"}],"year":"2018","citation":{"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.","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.","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.","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.","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.","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."},"type":"conference","page":"31-39","_id":"2857","conference":{"location":"Delft, Netherlands","start_date":"2018-06-24","name":"28th International Conference on Automated Planning and Scheduling","end_date":"2018-06-29"},"author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"orcid":"0000-0001-5859-2457","full_name":"Lettmann, Theodor","first_name":"Theodor","id":"315","last_name":"Lettmann"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"},{"orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik","first_name":"Marcel Dominik","id":"33176","last_name":"Wever"}],"publisher":"AAAI","publication":"Proceedings of the 1st ICAPS Workshop on Hierarchical Planning","file_date_updated":"2018-11-06T15:18:26Z","file":[{"date_created":"2018-11-06T15:18:26Z","file_name":"Mohr18ProgrammaticPlanning.pdf","access_level":"closed","creator":"wever","file_id":"5384","file_size":349958,"success":1,"relation":"main_file","content_type":"application/pdf","date_updated":"2018-11-06T15:18:26Z"}],"status":"public","has_accepted_license":"1","date_created":"2018-05-24T09:00:20Z","ddc":["000"],"user_id":"315"},{"title":"Stability of stochastic approximations with “controlled markov” noise and temporal difference learning","user_id":"66937","publication":"IEEE Transactions on Automatic Control","department":[{"_id":"355"}],"publisher":"IEEE","author":[{"id":"66937","last_name":"Ramaswamy","orcid":"https://orcid.org/ 0000-0001-7547-8111","full_name":"Ramaswamy, Arunselvan","first_name":"Arunselvan"},{"full_name":"Bhatnagar, Shalabh","first_name":"Shalabh","last_name":"Bhatnagar"}],"volume":64,"date_created":"2021-09-10T10:17:54Z","status":"public","_id":"24150","intvolume":" 64","date_updated":"2022-01-06T06:56:08Z","issue":"6","page":"2614-2620","year":"2018","type":"journal_article","citation":{"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.","short":"A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620.","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} }","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.","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.","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."},"language":[{"iso":"eng"}]},{"language":[{"iso":"eng"}],"type":"journal_article","year":"2018","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.","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.","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.","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.","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.","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} }"},"page":"737-742","issue":"4","_id":"24151","intvolume":" 2","date_updated":"2022-01-06T06:56:08Z","status":"public","date_created":"2021-09-10T10:19:07Z","volume":2,"publisher":"IEEE","author":[{"last_name":"Demirel","first_name":"Burak","full_name":"Demirel, Burak"},{"id":"66937","last_name":"Ramaswamy","full_name":"Ramaswamy, Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111","first_name":"Arunselvan"},{"first_name":"Daniel E","full_name":"Quevedo, Daniel E","last_name":"Quevedo"},{"last_name":"Karl","full_name":"Karl, Holger","first_name":"Holger"}],"publication":"IEEE Control Systems Letters","department":[{"_id":"355"}],"user_id":"66937","title":"Deepcas: A deep reinforcement learning algorithm for control-aware scheduling"},{"file":[{"content_type":"application/pdf","date_updated":"2018-11-06T15:15:38Z","relation":"main_file","success":1,"file_size":356132,"creator":"wever","file_id":"5383","access_level":"closed","date_created":"2018-11-06T15:15:38Z","file_name":"08456422.pdf"}],"publisher":"IEEE Computer Society","author":[{"first_name":"Felix","full_name":"Mohr, 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","status":"public","has_accepted_license":"1","date_created":"2018-04-23T11:40:20Z","user_id":"49109","ddc":["000"],"main_file_link":[{"url":"https://ieeexplore.ieee.org/abstract/document/8456422","open_access":"1"}],"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."},"type":"conference","_id":"2471","conference":{"end_date":"2018-07-07","name":"IEEE International Conference on Services Computing, SCC 2018","start_date":"2018-07-02","location":"San Francisco, CA, USA"},"department":[{"_id":"355"}],"project":[{"name":"SFB 901","_id":"1"},{"_id":"3","name":"SFB 901 - Project Area B"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"place":"San Francisco, CA, USA","title":"On-The-Fly Service Construction with Prototypes","language":[{"iso":"eng"}],"date_updated":"2022-01-06T06:56:32Z","oa":"1","doi":"10.1109/SCC.2018.00036"},{"_id":"3402","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.","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","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","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)."},"type":"journal_article","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"}],"ddc":["000"],"user_id":"15504","author":[{"last_name":"Melnikov","first_name":"Vitalik","full_name":"Melnikov, Vitalik"},{"id":"48129","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","first_name":"Eyke"}],"publication":"Machine Learning","file_date_updated":"2018-11-02T15:30:57Z","file":[{"date_created":"2018-11-02T15:30:57Z","file_name":"OnTheEffectivenessOfHeuristics.pdf","access_level":"closed","creator":"ups","file_id":"5305","file_size":1482882,"relation":"main_file","success":1,"content_type":"application/pdf","date_updated":"2018-11-02T15:30:57Z"}],"status":"public","has_accepted_license":"1","date_created":"2018-06-29T07:44:26Z","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"}],"publication_identifier":{"issn":["1573-0565"]},"project":[{"_id":"11","name":"SFB 901 - Subproject B3"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901","_id":"1"}]},{"file":[{"access_level":"closed","date_created":"2018-11-02T15:32:16Z","file_name":"ML-PlanAutomatedMachineLearnin.pdf","relation":"main_file","success":1,"date_updated":"2018-11-02T15:32:16Z","content_type":"application/pdf","file_id":"5306","creator":"ups","file_size":1070937}],"publisher":"Springer","author":[{"first_name":"Felix","full_name":"Mohr, Felix","last_name":"Mohr"},{"first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","id":"33176"},{"full_name":"Hüllermeier, Eyke","first_name":"Eyke","id":"48129","last_name":"Hüllermeier"}],"publication":"Machine Learning","keyword":["AutoML","Hierarchical Planning","HTN planning","ML-Plan"],"file_date_updated":"2018-11-02T15:32:16Z","has_accepted_license":"1","status":"public","date_created":"2018-07-08T14:06:14Z","article_type":"original","abstract":[{"text":"Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). 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.","lang":"eng"}],"user_id":"5786","ddc":["000"],"main_file_link":[{"url":"https://rdcu.be/3Nc2","open_access":"1"}],"citation":{"mla":"Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” Machine Learning, Springer, 2018, pp. 1495–515, doi:10.1007/s10994-018-5735-z.","bibtex":"@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine Learning via Hierarchical Planning}, DOI={10.1007/s10994-018-5735-z}, journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” Machine Learning, 2018, 1495–1515. https://doi.org/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. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z","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.","short":"F. Mohr, M.D. Wever, E. 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D., & Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19","ama":"Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification. 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