Bernhard Pfahringer

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Geoffrey HolmesUniversity of WaikatoVerified email at waikato.ac.nz
Eibe FrankProfessor, Department of Computer Science, University of WaikatoVerified email at cs.waikato.ac.nz
Albert BifetTélécom ParisTech & University of WaikatoVerified email at telecom-paristech.fr
Mark HallHonorary Research Associate, University of Waikato, New ZealandVerified email at cs.waikato.ac.nz
Ian H. WittenProfessor of Computer Science, University of Waikato, New ZealandVerified email at waikato.ac.nz
Jesse ReadÉcole PolytechniqueVerified email at polytechnique.edu
Stefan KramerProfessor of Computer Science, Johannes Gutenberg University MainzVerified email at informatik.uni-mainz.de
Heitor Murilo GomesLecturer in AI, Victoria University of WellingtonVerified email at vuw.ac.nz
Philipp KranenMicrosoft ResearchVerified email at microsoft.com
Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), MCML, GermanyVerified email at dbs.ifi.lmu.de
Hardy KremerDeloitte Analytics Institute, GermanyVerified email at deloitte.de
Indrė ŽliobaitėUniversity of HelsinkiVerified email at helsinki.fi
Joaquin VanschorenEindhoven University of TechnologyVerified email at tue.nl
Henry GoukAssistant Professor, University of EdinburghVerified email at ed.ac.uk
Felipe Bravo-MarquezAssistant Professor, University of ChileVerified email at dcc.uchile.cl
Ricard GavaldàUniversitat Politècnica de Catalunya + Amalfi AnalyticsVerified email at cs.upc.edu
Christoph Helmain silico toxicology gmbhVerified email at in-silico.ch
Jean Paul BarddalPrograma de Pós-Graduação em Informática (PPGIa), Pontifícia Universidade Católica do ParanáVerified email at ppgia.pucpr.br
Hendrik BlockeelDepartment of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
Jan N. van RijnLeiden UniversityVerified email at liacs.leidenuniv.nl

Bernhard Pfahringer

Professor of Computer Science, University of Waikato

Verified email at waikato.ac.nz - Homepage

Machine Learning Data Mining


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The WEKA data mining software: an update M Hall, E Frank, G Holmes, B Pfahringer, P Reutemann, IH Witten ACM SIGKDD explorations newsletter 11 (1), 10-18, 2009	25218	2009
Classifier chains for multi-label classification J Read, B Pfahringer, G Holmes, E Frank Machine learning 85, 333-359, 2011	2351	2011
Moa: Massive online analysis, a framework for stream classification and clustering A Bifet, G Holmes, B Pfahringer, P Kranen, H Kremer, T Jansen, T Seidl Proceedings of the first workshop on applications of pattern analysis, 44-50, 2010	2227	2010
Classifier chains for multi-label classification J Read, B Pfahringer, G Holmes, E Frank Machine Learning and Knowledge Discovery in Databases: European Conference …, 2009	1035	2009
New ensemble methods for evolving data streams A Bifet, G Holmes, B Pfahringer, R Kirkby, R Gavalda Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	827	2009
Weka-a machine learning workbench for data mining E Frank, M Hall, G Holmes, R Kirkby, B Pfahringer, IH Witten, L Trigg Data mining and knowledge discovery handbook, 1269-1277, 2010	806	2010
Multinomial naive bayes for text categorization revisited AM Kibriya, E Frank, B Pfahringer, G Holmes AI 2004: Advances in Artificial Intelligence: 17th Australian Joint …, 2005	635	2005
Multi-label classification using ensembles of pruned sets J Read, B Pfahringer, G Holmes 2008 eighth IEEE international conference on data mining, 995-1000, 2008	576	2008
Meta-Learning by Landmarking Various Learning Algorithms. B Pfahringer, H Bensusan, CG Giraud-Carrier ICML, 743-750, 2000	533	2000
Locally weighted naive bayes E Frank, M Hall, B Pfahringer arXiv preprint arXiv:1212.2487, 2012	496	2012
Regularisation of neural networks by enforcing lipschitz continuity H Gouk, E Frank, B Pfahringer, MJ Cree Machine Learning 110, 393-416, 2021	481	2021
WEKA---Experiences with a Java Open-Source Project RR Bouckaert, E Frank, MA Hall, G Holmes, B Pfahringer, P Reutemann, ... The Journal of Machine Learning Research 11, 2533-2541, 2010	457	2010
Leveraging bagging for evolving data streams A Bifet, G Holmes, B Pfahringer Machine Learning and Knowledge Discovery in Databases: European Conference …, 2010	450	2010
Active learning with drifting streaming data I Žliobaitė, A Bifet, B Pfahringer, G Holmes IEEE transactions on neural networks and learning systems 25 (1), 27-39, 2013	432	2013
Smote for regression L Torgo, RP Ribeiro, B Pfahringer, P Branco Portuguese conference on artificial intelligence, 378-389, 2013	329	2013
Winning the KDD99 classification cup: Bagged boosting B Pfahringer ACM SIGKDD Explorations Newsletter 1 (2), 65-66, 2000	328	2000
Machine learning for data streams: with practical examples in MOA A Bifet, R Gavalda, G Holmes, B Pfahringer MIT press, 2023	326	2023
Meka: a multi-label/multi-target extension to weka J Read, P Reutemann, B Pfahringer, G Holmes Journal of Machine Learning Research 17 (21), 1-5, 2016	326	2016
Multiclass alternating decision trees G Holmes, B Pfahringer, R Kirkby, E Frank, M Hall Machine Learning: ECML 2002: 13th European Conference on Machine Learning …, 2002	260	2002
Efficient online evaluation of big data stream classifiers A Bifet, G de Francisci Morales, J Read, G Holmes, B Pfahringer Proceedings of the 21th ACM SIGKDD international conference on knowledge …, 2015	228	2015

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