--- _id: '115' abstract: - lang: eng text: 'Whenever customers have to decide between different instances of the same product, they are interested in buying the best product. In contrast, companies are interested in reducing the construction effort (and usually as a consequence thereof, the quality) to gain profit. 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. We not only discuss how certificates are constructed and handled in service-oriented computing but also review the effects of certificates on the market from an economic perspective.' author: - first_name: Marie-Christine full_name: Jakobs, Marie-Christine last_name: Jakobs - first_name: Julia full_name: Krämer, Julia last_name: Krämer - first_name: Dirk full_name: van Straaten, Dirk id: '10311' last_name: van Straaten - first_name: Theodor full_name: Lettmann, Theodor id: '315' last_name: Lettmann orcid: 0000-0001-5859-2457 citation: ama: 'Jakobs M-C, Krämer J, van Straaten D, Lettmann T. Certification Matters for Service Markets. In: Marcelo De Barros, Janusz Klink,Tadeus Uhl TP, ed. The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION). ; 2017:7-12.' apa: Jakobs, M.-C., Krämer, J., van Straaten, D., & Lettmann, T. (2017). Certification Matters for Service Markets. In T. P. Marcelo De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION) (pp. 7–12). bibtex: '@inproceedings{Jakobs_Krämer_van Straaten_Lettmann_2017, title={Certification Matters for Service Markets}, booktitle={The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION)}, author={Jakobs, Marie-Christine and Krämer, Julia and van Straaten, Dirk and Lettmann, Theodor}, editor={Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas PrinzEditor}, year={2017}, pages={7–12} }' chicago: Jakobs, Marie-Christine, Julia Krämer, Dirk van Straaten, and Theodor Lettmann. “Certification Matters for Service Markets.” In The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), edited by Thomas Prinz Marcelo De Barros, Janusz Klink,Tadeus Uhl, 7–12, 2017. ieee: M.-C. Jakobs, J. Krämer, D. van Straaten, and T. Lettmann, “Certification Matters for Service Markets,” in The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2017, pp. 7–12. mla: Jakobs, Marie-Christine, et al. “Certification Matters for Service Markets.” The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), edited by Thomas Prinz Marcelo De Barros, Janusz Klink,Tadeus Uhl, 2017, pp. 7–12. short: 'M.-C. Jakobs, J. Krämer, D. van Straaten, T. Lettmann, in: T.P. Marcelo De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION), 2017, pp. 7–12.' date_created: 2017-10-17T12:41:14Z date_updated: 2022-01-06T06:51:02Z ddc: - '040' department: - _id: '77' - _id: '355' - _id: '179' editor: - first_name: Thomas Prinz full_name: Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas Prinz last_name: Marcelo De Barros, Janusz Klink,Tadeus Uhl file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-03-21T13:04:12Z date_updated: 2018-03-21T13:04:12Z file_id: '1564' file_name: 115-JakobsKraemerVanStraatenLettmann2017.pdf file_size: 133531 relation: main_file success: 1 file_date_updated: 2018-03-21T13:04:12Z has_accepted_license: '1' language: - iso: eng page: 7-12 project: - _id: '1' name: SFB 901 - _id: '10' name: SFB 901 - Subprojekt B2 - _id: '11' name: SFB 901 - Subproject B3 - _id: '12' name: SFB 901 - Subproject B4 - _id: '8' name: SFB 901 - Subproject A4 - _id: '2' name: SFB 901 - Project Area A - _id: '3' name: SFB 901 - Project Area B publication: The Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION) status: public title: Certification Matters for Service Markets type: conference user_id: '477' year: '2017' ... --- _id: '1158' abstract: - lang: eng 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. author: - first_name: Nina full_name: Seemann, Nina id: '65408' last_name: Seemann - first_name: Marie-Luis full_name: Merten, Marie-Luis last_name: Merten - first_name: Michaela full_name: Geierhos, Michaela id: '42496' last_name: Geierhos orcid: 0000-0002-8180-5606 - first_name: Doris full_name: Tophinke, Doris last_name: Tophinke - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: 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. Stroudsburg, PA, USA: Association for Computational Linguistics (ACL); 2017:40-45. doi:10.18653/v1/W17-2206' apa: 'Seemann, N., Merten, M.-L., Geierhos, M., Tophinke, D., & Hüllermeier, E. (2017). 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 (pp. 40–45). Stroudsburg, PA, USA: Association for Computational Linguistics (ACL). https://doi.org/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} }' chicago: 'Seemann, Nina, Marie-Luis Merten, Michaela Geierhos, Doris Tophinke, and Eyke 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, 40–45. Stroudsburg, PA, USA: Association for Computational Linguistics (ACL), 2017. https://doi.org/10.18653/v1/W17-2206.' 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. 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. short: 'N. 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.' conference: end_date: 2017-08-04 location: Vancouver, BC, Canada name: Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2017) start_date: 2017-07-31 date_created: 2018-01-31T15:32:33Z date_updated: 2022-01-06T06:51:03Z department: - _id: '36' - _id: '579' - _id: '115' - _id: '355' - _id: '615' doi: 10.18653/v1/W17-2206 language: - iso: eng page: 40-45 place: Stroudsburg, PA, USA project: - _id: '39' name: InterGramm publication: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature publication_status: published publisher: Association for Computational Linguistics (ACL) quality_controlled: '1' status: public title: Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German type: conference user_id: '13929' year: '2017' ... --- _id: '5694' author: - first_name: Nino Noel full_name: Schnitker, Nino Noel last_name: Schnitker citation: ama: Schnitker NN. Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn; 2017. apa: Schnitker, N. N. (2017). Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn. bibtex: '@book{Schnitker_2017, title={Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies}, publisher={Universität Paderborn}, author={Schnitker, Nino Noel}, year={2017} }' chicago: Schnitker, Nino Noel. Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn, 2017. ieee: N. N. Schnitker, Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn, 2017. mla: Schnitker, Nino Noel. Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies. Universität Paderborn, 2017. short: N.N. Schnitker, Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies, Universität Paderborn, 2017. date_created: 2018-11-15T08:10:48Z date_updated: 2022-01-06T07:02:35Z department: - _id: '355' language: - iso: ger project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publisher: Universität Paderborn status: public supervisor: - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier title: Genetischer Algorithmus zur Erstellung von Ensembles von Nested Dichotomies type: bachelorsthesis user_id: '477' year: '2017' ... --- _id: '5722' author: - first_name: Pritha full_name: Gupta, Pritha last_name: Gupta - first_name: Alexander full_name: Hetzer, Alexander id: '38209' last_name: Hetzer - first_name: Tanja full_name: Tornede, Tanja last_name: Tornede - first_name: Sebastian full_name: Gottschalk, Sebastian last_name: Gottschalk - first_name: Andreas full_name: Kornelsen, Andreas last_name: Kornelsen - first_name: Sebastian full_name: Osterbrink, Sebastian last_name: Osterbrink - first_name: Karlson full_name: Pfannschmidt, Karlson last_name: Pfannschmidt - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier citation: ama: 'Gupta P, Hetzer A, Tornede T, et al. jPL: A Java-based Software Framework for Preference Learning. In: ; 2017.' apa: 'Gupta, P., Hetzer, A., Tornede, T., Gottschalk, S., Kornelsen, A., Osterbrink, S., … Hüllermeier, E. (2017). jPL: A Java-based Software Framework for Preference Learning. Presented at the WDA 2017 Workshops: KDML, FGWM, IR, and FGDB, Rostock.' bibtex: '@inproceedings{Gupta_Hetzer_Tornede_Gottschalk_Kornelsen_Osterbrink_Pfannschmidt_Hüllermeier_2017, title={jPL: A Java-based Software Framework for Preference Learning}, author={Gupta, Pritha and Hetzer, Alexander and Tornede, Tanja and Gottschalk, Sebastian and Kornelsen, Andreas and Osterbrink, Sebastian and Pfannschmidt, Karlson and Hüllermeier, Eyke}, year={2017} }' chicago: 'Gupta, Pritha, Alexander Hetzer, Tanja Tornede, Sebastian Gottschalk, Andreas Kornelsen, Sebastian Osterbrink, Karlson Pfannschmidt, and Eyke Hüllermeier. “JPL: A Java-Based Software Framework for Preference Learning,” 2017.' ieee: 'P. Gupta et al., “jPL: A Java-based Software Framework for Preference Learning,” presented at the WDA 2017 Workshops: KDML, FGWM, IR, and FGDB, Rostock, 2017.' mla: 'Gupta, Pritha, et al. JPL: A Java-Based Software Framework for Preference Learning. 2017.' short: 'P. Gupta, A. Hetzer, T. Tornede, S. Gottschalk, A. Kornelsen, S. Osterbrink, K. Pfannschmidt, E. Hüllermeier, in: 2017.' conference: end_date: 13.09.2017 location: Rostock name: 'WDA 2017 Workshops: KDML, FGWM, IR, and FGDB' start_date: 11.09.2017 date_created: 2018-11-19T07:32:31Z date_updated: 2022-01-06T07:02:37Z department: - _id: '355' extern: '1' language: - iso: eng status: public title: 'jPL: A Java-based Software Framework for Preference Learning' type: conference_abstract user_id: '38209' year: '2017' ... --- _id: '5724' author: - first_name: Alexander full_name: Hetzer, Alexander id: '38209' last_name: Hetzer - first_name: Tanja full_name: Tornede, Tanja last_name: Tornede citation: ama: Hetzer A, Tornede T. 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. 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. ieee: A. Hetzer and T. Tornede, Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction. Universität Paderborn, 2017. mla: Hetzer, Alexander, and Tanja 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. date_created: 2018-11-19T07:49:13Z date_updated: 2022-01-06T07:02:37Z department: - _id: '355' - _id: '199' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publisher: Universität Paderborn status: public supervisor: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Kevin full_name: Tierney, Kevin last_name: Tierney title: Solving the Container Pre-Marshalling Problem using Reinforcement Learning and Structured Output Prediction type: mastersthesis user_id: '477' year: '2017' ... --- _id: '71' abstract: - lang: eng 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). author: - first_name: Mike full_name: Czech, Mike last_name: Czech - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Marie-Christine full_name: Jakobs, Marie-Christine last_name: Jakobs - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: 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' 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 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} }' 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. 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. 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. short: 'M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proceedings of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.' date_created: 2017-10-17T12:41:05Z date_updated: 2022-01-06T07:03:28Z ddc: - '000' department: - _id: '355' - _id: '77' doi: 10.1145/3121257.3121262 file: - access_level: closed content_type: application/pdf creator: ups date_created: 2018-11-02T14:24:29Z date_updated: 2018-11-02T14:24:29Z file_id: '5271' file_name: fsews17swan-swanmain1.pdf file_size: 822383 relation: main_file success: 1 file_date_updated: 2018-11-02T14:24:29Z has_accepted_license: '1' language: - iso: eng page: 23-26 project: - _id: '1' name: SFB 901 - _id: '12' name: SFB 901 - Subprojekt B4 - _id: '10' name: SFB 901 - Subproject B2 - _id: '3' name: SFB 901 - Project Area B - _id: '11' name: SFB 901 - Subproject B3 publication: Proceedings of the 3rd International Workshop on Software Analytics series_title: SWAN'17 status: public title: Predicting Rankings of Software Verification Tools type: conference user_id: '15504' year: '2017' ... --- _id: '72' abstract: - lang: eng 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). ' author: - first_name: Mike full_name: Czech, Mike last_name: Czech - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Marie-Christine full_name: Jakobs, Marie-Christine last_name: Jakobs - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software Verification Competitions.; 2017. apa: Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting Rankings of Software Verification Competitions. 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} }' chicago: Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike 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. 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. date_created: 2017-10-17T12:41:05Z date_updated: 2022-01-06T07:03:29Z ddc: - '000' department: - _id: '77' - _id: '355' file: - access_level: closed content_type: application/pdf creator: florida date_created: 2018-11-21T10:50:11Z date_updated: 2018-11-21T10:50:11Z file_id: '5782' file_name: "Predicting Rankings of So\x81ware Verification Competitions.pdf" file_size: 869984 relation: main_file success: 1 file_date_updated: 2018-11-21T10:50:11Z has_accepted_license: '1' language: - iso: eng project: - _id: '1' name: SFB 901 - _id: '11' name: SFB 901 - Subprojekt B3 - _id: '12' name: SFB 901 - Subprojekt B4 - _id: '3' name: SFB 901 - Project Area B status: public title: Predicting Rankings of Software Verification Competitions type: report user_id: '15504' year: '2017' ... --- _id: '10589' author: - first_name: J. full_name: Fürnkranz, J. last_name: Fürnkranz - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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). 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} }' chicago: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” In Encyclopedia of Machine Learning and Data Mining, 1000–1005, 2017. ieee: J. Fürnkranz and E. Hüllermeier, “Preference Learning,” in Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–1005. mla: Fürnkranz, J., and Eyke Hüllermeier. “Preference Learning.” Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–05. short: 'J. Fürnkranz, E. Hüllermeier, in: Encyclopedia of Machine Learning and Data Mining, 2017, pp. 1000–1005.' date_created: 2019-07-09T15:37:09Z date_updated: 2022-01-06T06:50:45Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 1000-1005 publication: Encyclopedia of Machine Learning and Data Mining status: public title: Preference Learning type: encyclopedia_article user_id: '49109' year: '2017' ... --- _id: '10784' author: - first_name: J. full_name: Fürnkranz, J. last_name: Fürnkranz - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: 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.' 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. 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} }' 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. 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. 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. short: 'J. Fürnkranz, E. Hüllermeier, in: C. Sammut, G.I. Webb (Eds.), Encyclopedia of Machine Learning and Data Mining, Springer, 2017, pp. 1000–1005.' date_created: 2019-07-10T15:44:32Z date_updated: 2022-01-06T06:50:50Z department: - _id: '34' - _id: '7' - _id: '355' editor: - first_name: C. full_name: Sammut, C. last_name: Sammut - first_name: G.I. full_name: Webb, G.I. last_name: Webb intvolume: ' 107' language: - iso: eng page: 1000-1005 publication: Encyclopedia of Machine Learning and Data Mining publisher: Springer status: public title: Preference Learning type: book_chapter user_id: '49109' volume: 107 year: '2017' ... --- _id: '1180' abstract: - lang: eng 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. author: - first_name: Marcel Dominik full_name: Wever, Marcel Dominik id: '33176' last_name: Wever orcid: ' https://orcid.org/0000-0001-9782-6818' - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Wever MD, Mohr F, Hüllermeier E. Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In: 27th Workshop Computational Intelligence. Dortmund; 2017.' apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational Intelligence. Dortmund.' bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2017, place={Dortmund}, title={Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}, booktitle={27th Workshop Computational Intelligence}, author={Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2017} }' chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” In 27th Workshop Computational Intelligence. Dortmund, 2017.' ieee: 'M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization,” in 27th Workshop Computational Intelligence, Dortmund, 2017.' mla: 'Wever, Marcel Dominik, et al. “Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization.” 27th Workshop Computational Intelligence, 2017.' short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence, Dortmund, 2017.' conference: end_date: 2017-11-24 location: Dortmund name: 27th Workshop Computational Intelligence start_date: 2017-11-23 date_created: 2018-02-22T07:19:18Z date_updated: 2022-01-06T06:51:09Z ddc: - '000' department: - _id: '355' file: - access_level: closed content_type: application/pdf creator: wever date_created: 2018-11-06T15:28:09Z date_updated: 2018-11-06T15:28:09Z file_id: '5387' file_name: CI Workshop AutoML.pdf file_size: 323589 relation: main_file success: 1 file_date_updated: 2018-11-06T15:28:09Z has_accepted_license: '1' language: - iso: eng main_file_link: - open_access: '1' url: https://publikationen.bibliothek.kit.edu/1000074341/4643874 oa: '1' place: Dortmund project: - _id: '1' name: SFB 901 - _id: '3' name: SFB 901 - Project Area B - _id: '10' name: SFB 901 - Subproject B2 publication: 27th Workshop Computational Intelligence publication_status: published status: public title: 'Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization' type: conference user_id: '49109' year: '2017' ... --- _id: '15397' author: - first_name: Vitaly full_name: Melnikov, Vitaly last_name: Melnikov - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Melnikov V, Hüllermeier E. Optimizing the structure of nested dichotomies. A comparison of two heuristics. In: Hoffmann F, Hüllermeier E, Mikut R, eds. In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany. KIT Scientific Publishing; 2017:1-12.' apa: Melnikov, V., & Hüllermeier, E. (2017). Optimizing the structure of nested dichotomies. A comparison of two heuristics. In F. Hoffmann, E. Hüllermeier, & R. Mikut (Eds.), in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany (pp. 1–12). KIT Scientific Publishing. bibtex: '@inproceedings{Melnikov_Hüllermeier_2017, title={Optimizing the structure of nested dichotomies. A comparison of two heuristics}, booktitle={in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany}, publisher={KIT Scientific Publishing}, author={Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.Editors}, year={2017}, pages={1–12} }' chicago: Melnikov, Vitaly, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies. A Comparison of Two Heuristics.” In In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann, Eyke Hüllermeier, and R. Mikut, 1–12. KIT Scientific Publishing, 2017. ieee: V. Melnikov and E. Hüllermeier, “Optimizing the structure of nested dichotomies. A comparison of two heuristics,” in in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, 2017, pp. 1–12. mla: Melnikov, Vitaly, and Eyke Hüllermeier. “Optimizing the Structure of Nested Dichotomies. A Comparison of Two Heuristics.” In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, edited by F. Hoffmann et al., KIT Scientific Publishing, 2017, pp. 1–12. short: 'V. Melnikov, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier, R. Mikut (Eds.), In Proceedings 27th Workshop Computational Intelligence, Dortmund Germany, KIT Scientific Publishing, 2017, pp. 1–12.' date_created: 2019-12-19T15:48:38Z date_updated: 2022-01-06T06:52:22Z department: - _id: '34' - _id: '7' - _id: '355' editor: - first_name: F. full_name: Hoffmann, F. last_name: Hoffmann - first_name: Eyke full_name: Hüllermeier, Eyke last_name: Hüllermeier - first_name: R. full_name: Mikut, R. last_name: Mikut language: - iso: eng page: 1-12 publication: in Proceedings 27th Workshop Computational Intelligence, Dortmund Germany publisher: KIT Scientific Publishing status: public title: Optimizing the structure of nested dichotomies. A comparison of two heuristics type: conference user_id: '49109' year: '2017' ... --- _id: '15399' author: - first_name: M. full_name: Czech, M. last_name: Czech - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: M.C. full_name: Jacobs, M.C. last_name: Jacobs - first_name: Heike full_name: Wehrheim, Heike last_name: Wehrheim citation: ama: 'Czech M, Hüllermeier E, Jacobs MC, Wehrheim H. Predicting rankings of software verification tools. In: In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany. ; 2017.' apa: Czech, M., Hüllermeier, E., Jacobs, M. C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany. bibtex: '@inproceedings{Czech_Hüllermeier_Jacobs_Wehrheim_2017, title={Predicting rankings of software verification tools}, booktitle={in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany}, author={Czech, M. and Hüllermeier, Eyke and Jacobs, M.C. and Wehrheim, Heike}, year={2017} }' chicago: Czech, M., Eyke Hüllermeier, M.C. Jacobs, and Heike Wehrheim. “Predicting Rankings of Software Verification Tools.” In In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017. ieee: M. Czech, E. Hüllermeier, M. C. Jacobs, and H. Wehrheim, “Predicting rankings of software verification tools,” in in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017. mla: Czech, M., et al. “Predicting Rankings of Software Verification Tools.” In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017. short: 'M. Czech, E. Hüllermeier, M.C. Jacobs, H. Wehrheim, in: In Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany, 2017.' date_created: 2019-12-19T15:59:42Z date_updated: 2022-01-06T06:52:22Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng publication: in Proceedings ESEC/FSE Workshops 2017 - 3rd ACM SIGSOFT, International Workshop on Software Analytics (SWAN 2017), Paderborn Germany status: public title: Predicting rankings of software verification tools type: conference user_id: '49109' year: '2017' ... --- _id: '15110' author: - first_name: Ines full_name: Couso, Ines last_name: Couso - first_name: D. full_name: Dubois, D. last_name: Dubois - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Couso I, Dubois D, Hüllermeier E. Maximum likelihood estimation and coarse data. In: In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain. Springer; 2017:3-16.' apa: Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum likelihood estimation and coarse data. In in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain (pp. 3–16). Springer. bibtex: '@inproceedings{Couso_Dubois_Hüllermeier_2017, title={Maximum likelihood estimation and coarse data}, booktitle={in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain}, publisher={Springer}, author={Couso, Ines and Dubois, D. and Hüllermeier, Eyke}, year={2017}, pages={3–16} }' chicago: Couso, Ines, D. Dubois, and Eyke Hüllermeier. “Maximum Likelihood Estimation and Coarse Data.” In In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, 3–16. Springer, 2017. ieee: I. Couso, D. Dubois, and E. Hüllermeier, “Maximum likelihood estimation and coarse data,” in in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, 2017, pp. 3–16. mla: Couso, Ines, et al. “Maximum Likelihood Estimation and Coarse Data.” In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, Springer, 2017, pp. 3–16. short: 'I. Couso, D. Dubois, E. Hüllermeier, in: In Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain, Springer, 2017, pp. 3–16.' date_created: 2019-11-21T16:38:39Z date_updated: 2022-01-06T06:52:15Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 3-16 publication: in Proceedings SUM 2017, 11th International Conference on Scalable Uncertainty Management, Granada, Spain publisher: Springer status: public title: Maximum likelihood estimation and coarse data type: conference user_id: '49109' year: '2017' ... --- _id: '10204' author: - first_name: Ralph full_name: Ewerth, Ralph last_name: Ewerth - first_name: M. full_name: Springstein, M. last_name: Springstein - first_name: E. full_name: Müller, E. last_name: Müller - first_name: A. full_name: Balz, A. last_name: Balz - first_name: J. full_name: Gehlhaar, J. last_name: Gehlhaar - first_name: T. full_name: Naziyok, T. last_name: Naziyok - first_name: K. full_name: Dembczynski, K. last_name: Dembczynski - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Ewerth R, Springstein M, Müller E, et al. Estimating relative depth in single images via rankboost. In: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017). ; 2017:919-924.' apa: Ewerth, R., Springstein, M., Müller, E., Balz, A., Gehlhaar, J., Naziyok, T., … Hüllermeier, E. (2017). Estimating relative depth in single images via rankboost. In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) (pp. 919–924). bibtex: '@inproceedings{Ewerth_Springstein_Müller_Balz_Gehlhaar_Naziyok_Dembczynski_Hüllermeier_2017, title={Estimating relative depth in single images via rankboost}, booktitle={Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017)}, author={Ewerth, Ralph and Springstein, M. and Müller, E. and Balz, A. and Gehlhaar, J. and Naziyok, T. and Dembczynski, K. and Hüllermeier, Eyke}, year={2017}, pages={919–924} }' chicago: Ewerth, Ralph, M. Springstein, E. Müller, A. Balz, J. Gehlhaar, T. Naziyok, K. Dembczynski, and Eyke Hüllermeier. “Estimating Relative Depth in Single Images via Rankboost.” In Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 919–24, 2017. ieee: R. Ewerth et al., “Estimating relative depth in single images via rankboost,” in Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–924. mla: Ewerth, Ralph, et al. “Estimating Relative Depth in Single Images via Rankboost.” Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–24. short: 'R. Ewerth, M. Springstein, E. Müller, A. Balz, J. Gehlhaar, T. Naziyok, K. Dembczynski, E. Hüllermeier, in: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017), 2017, pp. 919–924.' date_created: 2019-06-07T15:18:24Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 919-924 publication: Proc. IEEE Int. Conf. on Multimedia and Expo (ICME 2017) status: public title: Estimating relative depth in single images via rankboost type: conference user_id: '49109' year: '2017' ... --- _id: '10205' author: - first_name: Mohsen full_name: Ahmadi Fahandar, Mohsen last_name: Ahmadi Fahandar - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: Ines full_name: Couso, Ines last_name: Couso citation: ama: 'Ahmadi Fahandar M, Hüllermeier E, Couso I. Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In: Proc. 34th Int. Conf. on Machine Learning (ICML 2017). ; 2017:1078-1087.' apa: 'Ahmadi Fahandar, M., Hüllermeier, E., & Couso, I. (2017). Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening. In Proc. 34th Int. Conf. on Machine Learning (ICML 2017) (pp. 1078–1087).' bibtex: '@inproceedings{Ahmadi Fahandar_Hüllermeier_Couso_2017, title={Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening}, booktitle={Proc. 34th Int. Conf. on Machine Learning (ICML 2017)}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke and Couso, Ines}, year={2017}, pages={1078–1087} }' chicago: 'Ahmadi Fahandar, Mohsen, Eyke Hüllermeier, and Ines Couso. “Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening.” In Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 1078–87, 2017.' ieee: 'M. Ahmadi Fahandar, E. Hüllermeier, and I. Couso, “Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening,” in Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–1087.' mla: 'Ahmadi Fahandar, Mohsen, et al. “Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent  Coarsening.” Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–87.' short: 'M. Ahmadi Fahandar, E. Hüllermeier, I. Couso, in: Proc. 34th Int. Conf. on Machine Learning (ICML 2017), 2017, pp. 1078–1087.' date_created: 2019-06-07T15:22:01Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 1078-1087 publication: Proc. 34th Int. Conf. on Machine Learning (ICML 2017) status: public title: 'Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening' type: conference user_id: '49109' year: '2017' ... --- _id: '10206' author: - first_name: Felix full_name: Mohr, Felix last_name: Mohr - first_name: Theodor full_name: Lettmann, Theodor id: '315' last_name: Lettmann orcid: 0000-0001-5859-2457 - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Mohr F, Lettmann T, Hüllermeier E. Planning with Independent Task Networks. In: Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017). ; 2017:193-206. doi:10.1007/978-3-319-67190-1_15' apa: Mohr, F., Lettmann, T., & Hüllermeier, E. (2017). Planning with Independent Task Networks. In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) (pp. 193–206). https://doi.org/10.1007/978-3-319-67190-1_15 bibtex: '@inproceedings{Mohr_Lettmann_Hüllermeier_2017, title={Planning with Independent Task Networks}, DOI={10.1007/978-3-319-67190-1_15}, booktitle={Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017)}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier, Eyke}, year={2017}, pages={193–206} }' chicago: Mohr, Felix, Theodor Lettmann, and Eyke Hüllermeier. “Planning with Independent Task Networks.” In Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 193–206, 2017. https://doi.org/10.1007/978-3-319-67190-1_15. ieee: F. Mohr, T. Lettmann, and E. Hüllermeier, “Planning with Independent Task Networks,” in Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206. mla: Mohr, Felix, et al. “Planning with Independent Task Networks.” Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206, doi:10.1007/978-3-319-67190-1_15. short: 'F. Mohr, T. Lettmann, E. Hüllermeier, in: Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017), 2017, pp. 193–206.' date_created: 2019-06-07T15:24:16Z date_updated: 2022-01-06T06:50:31Z ddc: - '000' department: - _id: '7' - _id: '34' - _id: '355' doi: 10.1007/978-3-319-67190-1_15 file: - access_level: open_access content_type: application/pdf creator: lettmann date_created: 2020-02-28T12:50:18Z date_updated: 2020-02-28T12:50:18Z file_id: '16157' file_name: ki17.pdf file_size: 374421 relation: main_file file_date_updated: 2020-02-28T12:50:18Z has_accepted_license: '1' language: - iso: eng oa: '1' page: 193-206 publication: Proc. 40th Annual German Conference on Advances in Artificial Intelligence (KI 2017) status: public title: Planning with Independent Task Networks type: conference user_id: '315' year: '2017' ... --- _id: '10207' author: - first_name: M. full_name: Czech, M. last_name: Czech - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: M.-C. full_name: Jakobs, M.-C. last_name: Jakobs - first_name: Heike full_name: Wehrheim, Heike id: '573' last_name: Wehrheim citation: ama: 'Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting rankings of software verification tools. In: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017. ; 2017:23-26.' apa: Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting rankings of software verification tools. In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 (pp. 23–26). bibtex: '@inproceedings{Czech_Hüllermeier_Jakobs_Wehrheim_2017, title={Predicting rankings of software verification tools}, booktitle={Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017}, author={Czech, M. and Hüllermeier, Eyke and Jakobs, M.-C. and Wehrheim, Heike}, year={2017}, pages={23–26} }' chicago: Czech, M., Eyke Hüllermeier, M.-C. Jakobs, and Heike Wehrheim. “Predicting Rankings of Software Verification Tools.” In Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 23–26, 2017. ieee: M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting rankings of software verification tools,” in Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26. mla: Czech, M., et al. “Predicting Rankings of Software Verification Tools.” Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26. short: 'M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017, 2017, pp. 23–26.' date_created: 2019-06-07T15:27:47Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 23-26 publication: Proc. 3rd ACM SIGSOFT Int. I Workshop on Software Analytics (SWAN@ESEC/SIGSOFT FSE 2017 status: public title: Predicting rankings of software verification tools type: conference user_id: '49109' year: '2017' ... --- _id: '10208' author: - first_name: Ines full_name: Couso, Ines last_name: Couso - first_name: D. full_name: Dubois, D. last_name: Dubois - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Couso I, Dubois D, Hüllermeier E. Maximum Likelihood Estimation and Coarse Data. In: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017). ; 2017:3-16.' apa: Couso, I., Dubois, D., & Hüllermeier, E. (2017). Maximum Likelihood Estimation and Coarse Data. In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) (pp. 3–16). bibtex: '@inproceedings{Couso_Dubois_Hüllermeier_2017, title={Maximum Likelihood Estimation and Coarse Data}, booktitle={Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017)}, author={Couso, Ines and Dubois, D. and Hüllermeier, Eyke}, year={2017}, pages={3–16} }' chicago: Couso, Ines, D. Dubois, and Eyke Hüllermeier. “Maximum Likelihood Estimation and Coarse Data.” In Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 3–16, 2017. ieee: I. Couso, D. Dubois, and E. Hüllermeier, “Maximum Likelihood Estimation and Coarse Data,” in Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16. mla: Couso, Ines, et al. “Maximum Likelihood Estimation and Coarse Data.” Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16. short: 'I. Couso, D. Dubois, E. Hüllermeier, in: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017), 2017, pp. 3–16.' date_created: 2019-06-07T15:30:48Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng page: 3-16 publication: Proc. 11th Int. Conf. on Scalable Uncertainty Management (SUM 2017) status: public title: Maximum Likelihood Estimation and Coarse Data type: conference user_id: '49109' year: '2017' ... --- _id: '10209' author: - first_name: Mohsen full_name: Ahmadi Fahandar, Mohsen last_name: Ahmadi Fahandar - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier citation: ama: 'Ahmadi Fahandar M, Hüllermeier E. Learning to Rank based on Analogical Reasoning. In: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence. ; 2017.' apa: Ahmadi Fahandar, M., & Hüllermeier, E. (2017). Learning to Rank based on Analogical Reasoning. In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence. bibtex: '@inproceedings{Ahmadi Fahandar_Hüllermeier_2017, title={Learning to Rank based on Analogical Reasoning}, booktitle={Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence}, author={Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke}, year={2017} }' chicago: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” In Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017. ieee: M. Ahmadi Fahandar and E. Hüllermeier, “Learning to Rank based on Analogical Reasoning,” in Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017. mla: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Learning to Rank Based on Analogical Reasoning.” Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017. short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence, 2017.' date_created: 2019-06-07T15:33:14Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng publication: Proc. AAAI 2017, 32nd AAAI Conference on Artificial Intelligence status: public title: Learning to Rank based on Analogical Reasoning type: conference user_id: '49109' year: '2017' ... --- _id: '10212' author: - first_name: F. full_name: Hoffmann, F. last_name: Hoffmann - first_name: Eyke full_name: Hüllermeier, Eyke id: '48129' last_name: Hüllermeier - first_name: R. full_name: Mikut, R. last_name: Mikut citation: ama: 'Hoffmann F, Hüllermeier E, Mikut R. (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017. In: ; 2017.' apa: Hoffmann, F., Hüllermeier, E., & Mikut, R. (2017). (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017. bibtex: '@inproceedings{Hoffmann_Hüllermeier_Mikut_2017, title={(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017}, author={Hoffmann, F. and Hüllermeier, Eyke and Mikut, R.}, year={2017} }' chicago: Hoffmann, F., Eyke Hüllermeier, and R. Mikut. “(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017,” 2017. ieee: F. Hoffmann, E. Hüllermeier, and R. Mikut, “(Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017,” 2017. mla: Hoffmann, F., et al. (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017. 2017. short: 'F. Hoffmann, E. Hüllermeier, R. Mikut, in: 2017.' date_created: 2019-06-07T15:46:10Z date_updated: 2022-01-06T06:50:31Z department: - _id: '34' - _id: '7' - _id: '355' language: - iso: eng status: public title: (Hrsg.) Proceedings 27. Workshop Computational Intelligence, KIT Scientific Publishing, Karlsruhe, Germany 2017 type: conference user_id: '49109' year: '2017' ...