[{"ddc":["000"],"user_id":"49109","_id":"2479","publisher":"IEEE","has_accepted_license":"1","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"},"status":"public","oa":"1","place":"San Francisco, CA, USA","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"citation":{"chicago":"Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP) Towards the Automated Composition of Machine Learning Services.” In <i>SCC</i>. San Francisco, CA, USA: IEEE, 2018. <a href=\"https://doi.org/10.1109/SCC.2018.00039\">https://doi.org/10.1109/SCC.2018.00039</a>.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco, CA, USA, 2018.","apa":"Mohr, F., Wever, M. D., Hüllermeier, E., &#38; Faez, A. (2018). (WIP) Towards the Automated Composition of Machine Learning Services. In <i>SCC</i>. San Francisco, CA, USA: IEEE. <a href=\"https://doi.org/10.1109/SCC.2018.00039\">https://doi.org/10.1109/SCC.2018.00039</a>","ieee":"F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated Composition of Machine Learning Services,” in <i>SCC</i>, San Francisco, CA, USA, 2018.","ama":"Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition of Machine Learning Services. In: <i>SCC</i>. San Francisco, CA, USA: IEEE; 2018. doi:<a href=\"https://doi.org/10.1109/SCC.2018.00039\">10.1109/SCC.2018.00039</a>","bibtex":"@inproceedings{Mohr_Wever_Hüllermeier_Faez_2018, place={San Francisco, CA, USA}, title={(WIP) Towards the Automated Composition of Machine Learning Services}, DOI={<a href=\"https://doi.org/10.1109/SCC.2018.00039\">10.1109/SCC.2018.00039</a>}, 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.” <i>SCC</i>, IEEE, 2018, doi:<a href=\"https://doi.org/10.1109/SCC.2018.00039\">10.1109/SCC.2018.00039</a>."},"file_date_updated":"2018-11-06T15:08:39Z","doi":"10.1109/SCC.2018.00039","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://ieeexplore.ieee.org/document/8456425","open_access":"1"}],"date_updated":"2022-01-06T06:56:35Z","publication_status":"published","author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"full_name":"Wever, Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","last_name":"Wever","id":"33176"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"},{"last_name":"Faez","first_name":"Amin","full_name":"Faez, Amin"}],"title":"(WIP) Towards the Automated Composition of Machine Learning Services","year":"2018","department":[{"_id":"355"}],"type":"conference","date_created":"2018-04-24T08:34:52Z","file":[{"file_id":"5382","content_type":"application/pdf","relation":"main_file","date_updated":"2018-11-06T15:08:39Z","file_name":"08456425.pdf","access_level":"closed","file_size":237890,"date_created":"2018-11-06T15:08:39Z","creator":"wever"}],"publication":"SCC"},{"department":[{"_id":"7"},{"_id":"355"}],"type":"preprint","date_created":"2020-09-17T10:53:39Z","project":[{"_id":"52","name":"Computing Resources Provided by the Paderborn Center for Parallel Computing"}],"abstract":[{"lang":"eng","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."}],"citation":{"ama":"Pfannschmidt K, Gupta P, Hüllermeier E. Deep Architectures for Learning Context-dependent Ranking Functions. <i>arXiv:180305796</i>. 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} }","mla":"Pfannschmidt, Karlson, et al. “Deep Architectures for Learning Context-Dependent Ranking Functions.” <i>ArXiv:1803.05796</i>, 2018.","chicago":"Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Deep Architectures for Learning Context-Dependent Ranking Functions.” <i>ArXiv:1803.05796</i>, 2018.","short":"K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018).","apa":"Pfannschmidt, K., Gupta, P., &#38; Hüllermeier, E. (2018). Deep Architectures for Learning Context-dependent Ranking Functions. <i>ArXiv:1803.05796</i>.","ieee":"K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Deep Architectures for Learning Context-dependent Ranking Functions,” <i>arXiv:1803.05796</i>. 2018."},"publication":"arXiv:1803.05796","user_id":"13472","language":[{"iso":"eng"}],"_id":"19524","date_updated":"2022-01-06T06:54:06Z","author":[{"first_name":"Karlson","last_name":"Pfannschmidt","full_name":"Pfannschmidt, Karlson"},{"full_name":"Gupta, Pritha","last_name":"Gupta","first_name":"Pritha"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"year":"2018","status":"public","title":"Deep Architectures for Learning Context-dependent Ranking Functions"},{"publication":"Proceedings of the 1st ICAPS Workshop on Hierarchical Planning","department":[{"_id":"355"}],"type":"conference","date_created":"2018-05-24T09:00:20Z","file":[{"date_created":"2018-11-06T15:18:26Z","creator":"wever","file_id":"5384","content_type":"application/pdf","success":1,"relation":"main_file","date_updated":"2018-11-06T15:18:26Z","file_name":"Mohr18ProgrammaticPlanning.pdf","file_size":349958,"access_level":"closed"}],"date_updated":"2022-01-06T06:58:08Z","author":[{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"id":"315","first_name":"Theodor","last_name":"Lettmann","orcid":"0000-0001-5859-2457","full_name":"Lettmann, Theodor"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"},{"id":"33176","first_name":"Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"}],"title":"Programmatic Task Network Planning","year":"2018","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf"}],"project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"citation":{"chicago":"Mohr, Felix, Theodor Lettmann, Eyke Hüllermeier, and Marcel Dominik Wever. “Programmatic Task Network Planning.” In <i>Proceedings of the 1st ICAPS Workshop on Hierarchical Planning</i>, 31–39. AAAI, 2018.","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 <i>Proceedings of the 1st ICAPS Workshop on Hierarchical Planning</i>, Delft, Netherlands, 2018, pp. 31–39.","apa":"Mohr, F., Lettmann, T., Hüllermeier, E., &#38; Wever, M. D. (2018). Programmatic Task Network Planning. In <i>Proceedings of the 1st ICAPS Workshop on Hierarchical Planning</i> (pp. 31–39). Delft, Netherlands: AAAI.","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} }","ama":"Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning. In: <i>Proceedings of the 1st ICAPS Workshop on Hierarchical Planning</i>. AAAI; 2018:31-39.","mla":"Mohr, Felix, et al. “Programmatic Task Network Planning.” <i>Proceedings of the 1st ICAPS Workshop on Hierarchical Planning</i>, AAAI, 2018, pp. 31–39."},"file_date_updated":"2018-11-06T15:18:26Z","oa":"1","has_accepted_license":"1","conference":{"end_date":"2018-06-29","name":"28th International Conference on Automated Planning and Scheduling","start_date":"2018-06-24","location":"Delft, Netherlands"},"status":"public","user_id":"315","ddc":["000"],"_id":"2857","publisher":"AAAI","page":"31-39"},{"date_created":"2021-09-10T10:17:54Z","department":[{"_id":"355"}],"type":"journal_article","citation":{"mla":"Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stability of Stochastic Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning.” <i>IEEE Transactions on Automatic Control</i>, 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} }","ama":"Ramaswamy A, Bhatnagar S. Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. <i>IEEE Transactions on Automatic Control</i>. 2018;64(6):2614-2620.","ieee":"A. Ramaswamy and S. Bhatnagar, “Stability of stochastic approximations with ‘controlled markov’ noise and temporal difference learning,” <i>IEEE Transactions on Automatic Control</i>, vol. 64, no. 6, pp. 2614–2620, 2018.","apa":"Ramaswamy, A., &#38; Bhatnagar, S. (2018). Stability of stochastic approximations with “controlled markov” noise and temporal difference learning. <i>IEEE Transactions on Automatic Control</i>, <i>64</i>(6), 2614–2620.","chicago":"Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stability of Stochastic Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning.” <i>IEEE Transactions on Automatic Control</i> 64, no. 6 (2018): 2614–20.","short":"A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018) 2614–2620."},"issue":"6","publication":"IEEE Transactions on Automatic Control","_id":"24150","publisher":"IEEE","language":[{"iso":"eng"}],"page":"2614-2620","volume":64,"user_id":"66937","author":[{"first_name":"Arunselvan","last_name":"Ramaswamy","orcid":"https://orcid.org/ 0000-0001-7547-8111","full_name":"Ramaswamy, Arunselvan","id":"66937"},{"full_name":"Bhatnagar, Shalabh","first_name":"Shalabh","last_name":"Bhatnagar"}],"status":"public","year":"2018","title":"Stability of stochastic approximations with “controlled markov” noise and temporal difference learning","intvolume":"        64","date_updated":"2022-01-06T06:56:08Z"},{"author":[{"first_name":"Burak","last_name":"Demirel","full_name":"Demirel, Burak"},{"full_name":"Ramaswamy, Arunselvan","last_name":"Ramaswamy","first_name":"Arunselvan","orcid":"https://orcid.org/ 0000-0001-7547-8111","id":"66937"},{"full_name":"Quevedo, Daniel E","first_name":"Daniel E","last_name":"Quevedo"},{"full_name":"Karl, Holger","first_name":"Holger","last_name":"Karl"}],"year":"2018","status":"public","title":"Deepcas: A deep reinforcement learning algorithm for control-aware scheduling","intvolume":"         2","date_updated":"2022-01-06T06:56:08Z","_id":"24151","language":[{"iso":"eng"}],"publisher":"IEEE","page":"737-742","volume":2,"user_id":"66937","citation":{"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} }","ama":"Demirel B, Ramaswamy A, Quevedo DE, Karl H. Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. <i>IEEE Control Systems Letters</i>. 2018;2(4):737-742.","mla":"Demirel, Burak, et al. “Deepcas: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling.” <i>IEEE Control Systems Letters</i>, vol. 2, no. 4, IEEE, 2018, pp. 737–42.","short":"B. Demirel, A. Ramaswamy, D.E. Quevedo, H. Karl, IEEE Control Systems Letters 2 (2018) 737–742.","chicago":"Demirel, Burak, Arunselvan Ramaswamy, Daniel E Quevedo, and Holger Karl. “Deepcas: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling.” <i>IEEE Control Systems Letters</i> 2, no. 4 (2018): 737–42.","ieee":"B. Demirel, A. Ramaswamy, D. E. Quevedo, and H. Karl, “Deepcas: A deep reinforcement learning algorithm for control-aware scheduling,” <i>IEEE Control Systems Letters</i>, vol. 2, no. 4, pp. 737–742, 2018.","apa":"Demirel, B., Ramaswamy, A., Quevedo, D. E., &#38; Karl, H. (2018). Deepcas: A deep reinforcement learning algorithm for control-aware scheduling. <i>IEEE Control Systems Letters</i>, <i>2</i>(4), 737–742."},"issue":"4","publication":"IEEE Control Systems Letters","date_created":"2021-09-10T10:19:07Z","department":[{"_id":"355"}],"type":"journal_article"},{"publisher":"IEEE Computer Society","_id":"2471","user_id":"49109","ddc":["000"],"status":"public","conference":{"location":"San Francisco, CA, USA","name":"IEEE International Conference on Services Computing, SCC 2018","start_date":"2018-07-02","end_date":"2018-07-07"},"has_accepted_license":"1","place":"San Francisco, CA, USA","oa":"1","file_date_updated":"2018-11-06T15:15:38Z","citation":{"ama":"Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes. In: <i>SCC</i>. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:<a href=\"https://doi.org/10.1109/SCC.2018.00036\">10.1109/SCC.2018.00036</a>","bibtex":"@inproceedings{Mohr_Wever_Hüllermeier_2018, place={San Francisco, CA, USA}, title={On-The-Fly Service Construction with Prototypes}, DOI={<a href=\"https://doi.org/10.1109/SCC.2018.00036\">10.1109/SCC.2018.00036</a>}, booktitle={SCC}, publisher={IEEE Computer Society}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018} }","mla":"Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.” <i>SCC</i>, IEEE Computer Society, 2018, doi:<a href=\"https://doi.org/10.1109/SCC.2018.00036\">10.1109/SCC.2018.00036</a>.","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “On-The-Fly Service Construction with Prototypes.” In <i>SCC</i>. San Francisco, CA, USA: IEEE Computer Society, 2018. <a href=\"https://doi.org/10.1109/SCC.2018.00036\">https://doi.org/10.1109/SCC.2018.00036</a>.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San Francisco, CA, USA, 2018.","apa":"Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (2018). On-The-Fly Service Construction with Prototypes. In <i>SCC</i>. San Francisco, CA, USA: IEEE Computer Society. <a href=\"https://doi.org/10.1109/SCC.2018.00036\">https://doi.org/10.1109/SCC.2018.00036</a>","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction with Prototypes,” in <i>SCC</i>, San Francisco, CA, USA, 2018."},"project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"}],"main_file_link":[{"url":"https://ieeexplore.ieee.org/abstract/document/8456422","open_access":"1"}],"language":[{"iso":"eng"}],"doi":"10.1109/SCC.2018.00036","title":"On-The-Fly Service Construction with Prototypes","year":"2018","author":[{"last_name":"Mohr","first_name":"Felix","full_name":"Mohr, Felix"},{"id":"33176","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke","id":"48129"}],"date_updated":"2022-01-06T06:56:32Z","file":[{"file_id":"5383","success":1,"content_type":"application/pdf","file_name":"08456422.pdf","access_level":"closed","file_size":356132,"relation":"main_file","date_updated":"2018-11-06T15:15:38Z","date_created":"2018-11-06T15:15:38Z","creator":"wever"}],"date_created":"2018-04-23T11:40:20Z","type":"conference","department":[{"_id":"355"}],"publication":"SCC"},{"project":[{"name":"SFB 901 - Subproject B3","_id":"11"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901","_id":"1"}],"file_date_updated":"2018-11-02T15:30:57Z","citation":{"short":"V. Melnikov, E. Hüllermeier, Machine Learning (2018).","chicago":"Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics for Learning Nested Dichotomies: An Empirical Analysis.” <i>Machine Learning</i>, 2018. <a href=\"https://doi.org/10.1007/s10994-018-5733-1\">https://doi.org/10.1007/s10994-018-5733-1</a>.","apa":"Melnikov, V., &#38; Hüllermeier, E. (2018). On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. <i>Machine Learning</i>. <a href=\"https://doi.org/10.1007/s10994-018-5733-1\">https://doi.org/10.1007/s10994-018-5733-1</a>","ieee":"V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis,” <i>Machine Learning</i>, 2018.","ama":"Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. <i>Machine Learning</i>. 2018. doi:<a href=\"https://doi.org/10.1007/s10994-018-5733-1\">10.1007/s10994-018-5733-1</a>","bibtex":"@article{Melnikov_Hüllermeier_2018, title={On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis}, DOI={<a href=\"https://doi.org/10.1007/s10994-018-5733-1\">10.1007/s10994-018-5733-1</a>}, 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.” <i>Machine Learning</i>, 2018, doi:<a href=\"https://doi.org/10.1007/s10994-018-5733-1\">10.1007/s10994-018-5733-1</a>."},"has_accepted_license":"1","status":"public","ddc":["000"],"user_id":"15504","_id":"3402","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"}],"publication":"Machine Learning","type":"journal_article","department":[{"_id":"355"}],"file":[{"access_level":"closed","file_size":1482882,"file_name":"OnTheEffectivenessOfHeuristics.pdf","date_updated":"2018-11-02T15:30:57Z","relation":"main_file","content_type":"application/pdf","success":1,"file_id":"5305","creator":"ups","date_created":"2018-11-02T15:30:57Z"}],"date_created":"2018-06-29T07:44:26Z","date_updated":"2022-01-06T06:59:14Z","title":"On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis","year":"2018","publication_identifier":{"issn":["1573-0565"]},"author":[{"full_name":"Melnikov, Vitalik","last_name":"Melnikov","first_name":"Vitalik"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"doi":"10.1007/s10994-018-5733-1","language":[{"iso":"eng"}]},{"publication":"Machine Learning","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"}],"file":[{"date_created":"2018-11-02T15:32:16Z","creator":"ups","file_id":"5306","content_type":"application/pdf","success":1,"relation":"main_file","date_updated":"2018-11-02T15:32:16Z","file_name":"ML-PlanAutomatedMachineLearnin.pdf","file_size":1070937,"access_level":"closed"}],"date_created":"2018-07-08T14:06:14Z","keyword":["AutoML","Hierarchical Planning","HTN planning","ML-Plan"],"type":"journal_article","department":[{"_id":"355"},{"_id":"34"},{"_id":"7"},{"_id":"26"}],"year":"2018","title":"ML-Plan: Automated Machine Learning via Hierarchical Planning","author":[{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"33176","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"id":"48129","first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke"}],"publication_identifier":{"eissn":["1573-0565"],"issn":["0885-6125"]},"publication_status":"epub_ahead","date_updated":"2022-01-06T06:59:21Z","article_type":"original","main_file_link":[{"open_access":"1","url":"https://rdcu.be/3Nc2"}],"language":[{"iso":"eng"}],"doi":"10.1007/s10994-018-5735-z","file_date_updated":"2018-11-02T15:32:16Z","citation":{"ama":"Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical Planning. <i>Machine Learning</i>. Published online 2018:1495-1515. doi:<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>","short":"F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” <i>Machine Learning</i>, 2018, 1495–1515. <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">https://doi.org/10.1007/s10994-018-5735-z</a>.","bibtex":"@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine Learning via Hierarchical Planning}, DOI={<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>}, journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }","apa":"Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (2018). ML-Plan: Automated Machine Learning via Hierarchical Planning. <i>Machine Learning</i>, 1495–1515. <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">https://doi.org/10.1007/s10994-018-5735-z</a>","mla":"Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical Planning.” <i>Machine Learning</i>, Springer, 2018, pp. 1495–515, doi:<a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>.","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning via Hierarchical Planning,” <i>Machine Learning</i>, pp. 1495–1515, 2018, doi: <a href=\"https://doi.org/10.1007/s10994-018-5735-z\">10.1007/s10994-018-5735-z</a>."},"project":[{"_id":"1","name":"SFB 901"},{"_id":"3","name":"SFB 901 - Project Area B"},{"name":"SFB 901 - Subproject B2","_id":"10"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"oa":"1","status":"public","conference":{"end_date":"2018-09-14","start_date":"2018-09-10","name":"European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases","location":"Dublin, Ireland"},"has_accepted_license":"1","page":"1495-1515","_id":"3510","publisher":"Springer","user_id":"5786","ddc":["000"]},{"publication":"Proceedings of the Symposium on Intelligent Data Analysis","date_created":"2018-07-13T15:29:15Z","file":[{"creator":"wever","date_created":"2018-11-06T15:23:02Z","file_name":"Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf","access_level":"closed","file_size":1348768,"relation":"main_file","date_updated":"2018-11-06T15:23:02Z","file_id":"5385","content_type":"application/pdf","success":1}],"department":[{"_id":"355"}],"type":"conference","author":[{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"id":"33176","full_name":"Wever, Marcel Dominik","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"year":"2018","title":"Reduction Stumps for Multi-Class Classification","date_updated":"2022-01-06T06:59:25Z","publication_status":"accepted","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19","open_access":"1"}],"doi":"10.1007/978-3-030-01768-2_19","citation":{"chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Reduction Stumps for Multi-Class Classification.” In <i>Proceedings of the Symposium on Intelligent Data Analysis</i>. ‘s-Hertogenbosch, the Netherlands, n.d. <a href=\"https://doi.org/10.1007/978-3-030-01768-2_19\">https://doi.org/10.1007/978-3-030-01768-2_19</a>.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.","apa":"Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (n.d.). Reduction Stumps for Multi-Class Classification. In <i>Proceedings of the Symposium on Intelligent Data Analysis</i>. ‘s-Hertogenbosch, the Netherlands. <a href=\"https://doi.org/10.1007/978-3-030-01768-2_19\">https://doi.org/10.1007/978-3-030-01768-2_19</a>","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-Class Classification,” in <i>Proceedings of the Symposium on Intelligent Data Analysis</i>, ‘s-Hertogenbosch, the Netherlands.","ama":"Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification. In: <i>Proceedings of the Symposium on Intelligent Data Analysis</i>. ‘s-Hertogenbosch, the Netherlands. doi:<a href=\"https://doi.org/10.1007/978-3-030-01768-2_19\">10.1007/978-3-030-01768-2_19</a>","bibtex":"@inproceedings{Mohr_Wever_Hüllermeier, place={‘s-Hertogenbosch, the Netherlands}, title={Reduction Stumps for Multi-Class Classification}, DOI={<a href=\"https://doi.org/10.1007/978-3-030-01768-2_19\">10.1007/978-3-030-01768-2_19</a>}, 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.” <i>Proceedings of the Symposium on Intelligent Data Analysis</i>, doi:<a href=\"https://doi.org/10.1007/978-3-030-01768-2_19\">10.1007/978-3-030-01768-2_19</a>."},"file_date_updated":"2018-11-06T15:23:02Z","project":[{"name":"SFB 901","_id":"1"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"SFB 901 - Project Area B","_id":"3"}],"quality_controlled":"1","place":"‘s-Hertogenbosch, the Netherlands","oa":"1","conference":{"name":"Symposium on Intelligent Data Analysis","start_date":"2018-10-24","location":"‘s-Hertogenbosch, the Netherlands","end_date":"2018-10-26"},"status":"public","has_accepted_license":"1","_id":"3552","ddc":["000"],"user_id":"49109"},{"language":[{"iso":"eng"}],"main_file_link":[{"url":"https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx"}],"author":[{"orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","first_name":"Marcel Dominik","full_name":"Wever, Marcel Dominik","id":"33176"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}],"title":"ML-Plan for Unlimited-Length Machine Learning Pipelines","year":"2018","date_updated":"2022-01-06T06:59:46Z","date_created":"2018-08-09T06:14:54Z","file":[{"relation":"main_file","date_updated":"2018-08-09T06:14:43Z","file_name":"38.pdf","file_size":297811,"access_level":"open_access","file_id":"3853","content_type":"application/pdf","creator":"wever","date_created":"2018-08-09T06:14:43Z"}],"department":[{"_id":"355"}],"type":"conference","keyword":["automated machine learning","complex pipelines","hierarchical planning"],"publication":"ICML 2018 AutoML Workshop","abstract":[{"text":"In automated machine learning (AutoML), the process of engineering machine learning applications with respect to a specific problem is (partially) automated.\r\nVarious AutoML tools have already been introduced to provide out-of-the-box machine learning functionality.\r\nMore specifically, by selecting machine learning algorithms and optimizing their hyperparameters, these tools produce a machine learning pipeline tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT follows an evolutionary approach, it suffers from performance issues when dealing with larger datasets.\r\nIn this paper, we present an alternative approach leveraging a hierarchical planning to configure machine learning pipelines that are unlimited in length.\r\nWe evaluate our approach and find its performance to be competitive with other AutoML tools, including TPOT.","lang":"eng"}],"_id":"3852","urn":"38527","user_id":"49109","ddc":["006"],"conference":{"end_date":"2018-07-15","location":"Stockholm, Sweden","start_date":"2018-07-10","name":"ICML 2018 AutoML Workshop"},"status":"public","has_accepted_license":"1","oa":"1","citation":{"chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” In <i>ICML 2018 AutoML Workshop</i>, 2018.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine Learning Pipelines,” in <i>ICML 2018 AutoML Workshop</i>, Stockholm, Sweden, 2018.","apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). ML-Plan for Unlimited-Length Machine Learning Pipelines. In <i>ICML 2018 AutoML Workshop</i>. Stockholm, Sweden.","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }","ama":"Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning Pipelines. In: <i>ICML 2018 AutoML Workshop</i>. ; 2018.","mla":"Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning Pipelines.” <i>ICML 2018 AutoML Workshop</i>, 2018."},"file_date_updated":"2018-08-09T06:14:43Z","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"quality_controlled":"1"},{"place":"Kyoto, Japan","oa":"1","file_date_updated":"2018-11-02T14:33:54Z","citation":{"ama":"Wever MD, Mohr F, Hüllermeier E. Ensembles of Evolved Nested Dichotomies for Classification. In: <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>. Kyoto, Japan: ACM; 2018. doi:<a href=\"https://doi.org/10.1145/3205455.3205562\">10.1145/3205455.3205562</a>","bibtex":"@inproceedings{Wever_Mohr_Hüllermeier_2018, place={Kyoto, Japan}, title={Ensembles of Evolved Nested Dichotomies for Classification}, DOI={<a href=\"https://doi.org/10.1145/3205455.3205562\">10.1145/3205455.3205562</a>}, booktitle={Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018}, publisher={ACM}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }","mla":"Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for Classification.” <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>, ACM, 2018, doi:<a href=\"https://doi.org/10.1145/3205455.3205562\">10.1145/3205455.3205562</a>.","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of Evolved Nested Dichotomies for Classification.” In <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>. Kyoto, Japan: ACM, 2018. <a href=\"https://doi.org/10.1145/3205455.3205562\">https://doi.org/10.1145/3205455.3205562</a>.","short":"M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, Kyoto, Japan, 2018.","apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). Ensembles of Evolved Nested Dichotomies for Classification. In <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>. Kyoto, Japan: ACM. <a href=\"https://doi.org/10.1145/3205455.3205562\">https://doi.org/10.1145/3205455.3205562</a>","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies for Classification,” in <i>Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018</i>, Kyoto, Japan, 2018."},"project":[{"_id":"1","name":"SFB 901"},{"_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"}],"publisher":"ACM","_id":"2109","user_id":"33176","ddc":["000"],"status":"public","conference":{"location":"Kyoto, Japan","name":"GECCO 2018","start_date":"2018-07-15","end_date":"2018-07-19"},"has_accepted_license":"1","file":[{"content_type":"application/pdf","success":1,"file_id":"5275","file_size":875404,"access_level":"closed","file_name":"p561-wever.pdf","date_updated":"2018-11-02T14:33:54Z","relation":"main_file","date_created":"2018-11-02T14:33:54Z","creator":"ups"}],"date_created":"2018-03-31T13:51:23Z","keyword":["Classification","Hierarchical Decomposition","Indirect Encoding"],"type":"conference","department":[{"_id":"355"}],"publication":"Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018","abstract":[{"lang":"eng","text":"In multinomial classification, reduction techniques are commonly used to decompose the original learning problem into several simpler problems. 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."}],"main_file_link":[{"open_access":"1","url":"https://dl.acm.org/citation.cfm?doid=3205455.3205562"}],"language":[{"iso":"eng"}],"doi":"10.1145/3205455.3205562","title":"Ensembles of Evolved Nested Dichotomies for Classification","year":"2018","author":[{"id":"33176","last_name":"Wever","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","full_name":"Wever, Marcel Dominik"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"first_name":"Eyke","last_name":"Hüllermeier","full_name":"Hüllermeier, Eyke","id":"48129"}],"publication_status":"published","date_updated":"2022-01-06T06:54:45Z"},{"type":"preprint","oa":"1","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"date_created":"2020-08-07T11:38:10Z","project":[{"name":"SFB 901","_id":"1"},{"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"}],"citation":{"short":"M.D. Wever, F. Mohr, E. Hüllermeier, (2018).","chicago":"Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label Classification Based on ML-Plan.” Arxiv, 2018.","apa":"Wever, M. D., Mohr, F., &#38; Hüllermeier, E. (2018). <i>Automated Multi-Label Classification based on ML-Plan</i>. Arxiv.","ieee":"M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification based on ML-Plan.” Arxiv, 2018.","ama":"Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based on ML-Plan. Published online 2018.","bibtex":"@article{Wever_Mohr_Hüllermeier_2018, title={Automated Multi-Label Classification based on ML-Plan}, publisher={Arxiv}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }","mla":"Wever, Marcel Dominik, et al. <i>Automated Multi-Label Classification Based on ML-Plan</i>. Arxiv, 2018."},"user_id":"5786","main_file_link":[{"open_access":"1","url":"https://arxiv.org/pdf/1811.04060.pdf"}],"_id":"17713","language":[{"iso":"eng"}],"publisher":"Arxiv","date_updated":"2022-01-06T06:53:17Z","year":"2018","title":"Automated Multi-Label Classification based on ML-Plan","status":"public","author":[{"id":"33176","first_name":"Marcel Dominik","orcid":" https://orcid.org/0000-0001-9782-6818","last_name":"Wever","full_name":"Wever, Marcel Dominik"},{"full_name":"Mohr, Felix","first_name":"Felix","last_name":"Mohr"},{"full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke","id":"48129"}]},{"date_created":"2020-08-07T11:40:13Z","department":[{"_id":"34"},{"_id":"355"},{"_id":"26"}],"oa":"1","type":"preprint","citation":{"bibtex":"@article{Mohr_Wever_Hüllermeier_2018, title={Automated machine learning service composition}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2018} }","ama":"Mohr F, Wever MD, Hüllermeier E. Automated machine learning service composition. Published online 2018.","mla":"Mohr, Felix, et al. <i>Automated Machine Learning Service Composition</i>. 2018.","short":"F. Mohr, M.D. Wever, E. Hüllermeier, (2018).","chicago":"Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Automated Machine Learning Service Composition,” 2018.","ieee":"F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service composition.” 2018.","apa":"Mohr, F., Wever, M. D., &#38; Hüllermeier, E. (2018). <i>Automated machine learning service composition</i>."},"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area B","_id":"3"},{"_id":"10","name":"SFB 901 - Subproject B2"},{"name":"Computing Resources Provided by the Paderborn Center for Parallel Computing","_id":"52"}],"_id":"17714","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://arxiv.org/pdf/1809.00486.pdf"}],"user_id":"5786","author":[{"full_name":"Mohr, Felix","last_name":"Mohr","first_name":"Felix"},{"full_name":"Wever, Marcel Dominik","last_name":"Wever","orcid":" https://orcid.org/0000-0001-9782-6818","first_name":"Marcel Dominik","id":"33176"},{"id":"48129","full_name":"Hüllermeier, Eyke","last_name":"Hüllermeier","first_name":"Eyke"}],"year":"2018","status":"public","title":"Automated machine learning service composition","date_updated":"2022-01-06T06:53:17Z"},{"user_id":"33176","publisher":"Universität Paderborn","_id":"5693","language":[{"iso":"eng"}],"date_updated":"2022-01-06T07:02:35Z","author":[{"last_name":"Graf","first_name":"Helena","full_name":"Graf, Helena","id":"52640"}],"status":"public","year":"2018","title":"Ranking of Classification Algorithms in AutoML","department":[{"_id":"355"}],"type":"bachelorsthesis","date_created":"2018-11-15T08:06:41Z","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area B","_id":"3"},{"name":"SFB 901 - Subproject B2","_id":"10"}],"citation":{"mla":"Graf, Helena. <i>Ranking of Classification Algorithms in AutoML</i>. Universität Paderborn, 2018.","apa":"Graf, H. (2018). <i>Ranking of Classification Algorithms in AutoML</i>. Universität Paderborn.","ieee":"H. Graf, <i>Ranking of Classification Algorithms in AutoML</i>. Universität Paderborn, 2018.","ama":"Graf H. <i>Ranking of Classification Algorithms in AutoML</i>. Universität Paderborn; 2018.","short":"H. Graf, Ranking of Classification Algorithms in AutoML, Universität Paderborn, 2018.","chicago":"Graf, Helena. <i>Ranking of Classification Algorithms in AutoML</i>. Universität Paderborn, 2018.","bibtex":"@book{Graf_2018, title={Ranking of Classification Algorithms in AutoML}, publisher={Universität Paderborn}, author={Graf, Helena}, year={2018} }"},"supervisor":[{"id":"48129","last_name":"Hüllermeier","first_name":"Eyke","full_name":"Hüllermeier, Eyke"}]},{"status":"public","year":"2018","title":"Learning about learning curves from dataset properties","author":[{"full_name":"Scheibl, Manuel","last_name":"Scheibl","first_name":"Manuel"}],"date_updated":"2022-01-06T07:02:47Z","publisher":"Universität Paderborn","_id":"5936","language":[{"iso":"eng"}],"user_id":"477","citation":{"chicago":"Scheibl, Manuel. <i>Learning about Learning Curves from Dataset Properties</i>. Universität Paderborn, 2018.","short":"M. Scheibl, Learning about Learning Curves from Dataset Properties, Universität Paderborn, 2018.","apa":"Scheibl, M. (2018). <i>Learning about learning curves from dataset properties</i>. Universität Paderborn.","ieee":"M. Scheibl, <i>Learning about learning curves from dataset properties</i>. Universität Paderborn, 2018.","ama":"Scheibl M. <i>Learning about Learning Curves from Dataset Properties</i>. Universität Paderborn; 2018.","bibtex":"@book{Scheibl_2018, title={Learning about learning curves from dataset properties}, publisher={Universität Paderborn}, author={Scheibl, Manuel}, year={2018} }","mla":"Scheibl, Manuel. <i>Learning about Learning Curves from Dataset Properties</i>. 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