Bernhard Pfahringer - Citazioni di Google Scholar

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Coautori

Geoffrey HolmesEmail verificata su waikato.ac.nz
Eibe FrankProfessor, Department of Computer Science, University of WaikatoEmail verificata su cs.waikato.ac.nz
Albert BifetTélécom ParisTechEmail verificata su telecom-paristech.fr
Mark HallHonorary Research Associate, University of Waikato, New ZealandEmail verificata su cs.waikato.ac.nz
Ian H. WittenProfessor of Computer Science, University of Waikato, New ZealandEmail verificata su cs.waikato.ac.nz
Jesse ReadÉcole PolytechniqueEmail verificata su polytechnique.edu
Stefan KramerProfessor of Computer Science, Johannes Gutenberg University MainzEmail verificata su informatik.uni-mainz.de
Joaquin VanschorenEindhoven University of TechnologyEmail verificata su tue.nl
Indrė ŽliobaitėAssistant Professor, University of HelsinkiEmail verificata su helsinki.fi
Christoph Helmain silico toxicology gmbhEmail verificata su in-silico.ch
Ricard GavaldàUniversitat Politècnica de CatalunyaEmail verificata su cs.upc.edu
Felipe Bravo-MarquezAssistant Professor, University of ChileEmail verificata su dcc.uchile.cl
Hendrik BlockeelDepartment of Computer Science, KU LeuvenEmail verificata su cs.kuleuven.be
Philipp KranenMicrosoft ResearchEmail verificata su microsoft.com
Jan N. van RijnLeiden UniversityEmail verificata su liacs.leidenuniv.nl
Quan SunThe University of WaikatoEmail verificata su students.waikato.ac.nz
Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), GermanyEmail verificata su dbs.ifi.lmu.de
Hardy KremerDeloitte Analytics Institute, GermanyEmail verificata su deloitte.de
Jean Paul BarddalPrograma de Pós-Graduação em Informática (PPGIa), Pontifícia Universidade Católica do ParanáEmail verificata su ppgia.pucpr.br
Gerhard WidmerProfessor of Computer Science, Johannes Kepler University LinzEmail verificata su jku.at

Bernhard Pfahringer

Professor of Computer Science, University of Waikato

Email verificata su waikato.ac.nz - Home page

Machine Learning Data Mining


Titolo	Citata da	Anno
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	20211	2009
Moa: Massive online analysis A Bifet, G Holmes, R Kirkby, B Pfahringer Journal of Machine Learning Research 11 (May), 1601-1604, 2010	1365	2010
Classifier chains for multi-label classification J Read, B Pfahringer, G Holmes, E Frank Machine learning 85 (3), 333, 2011	1163	2011
Classifier chains for multi-label classification J Read, B Pfahringer, G Holmes, E Frank Joint European Conference on Machine Learning and Knowledge Discovery in …, 2009	617	2009
New ensemble methods for evolving data streams A Bifet, G Holmes, B Pfahringer, R Kirkby, R Gavaldà Proceedings of the 15th ACM SIGKDD international conference on Knowledge …, 2009	569	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, 2009	477	2009
Meta-Learning by Landmarking Various Learning Algorithms. B Pfahringer, H Bensusan, CG Giraud-Carrier ICML, 743-750, 2000	407	2000
Locally weighted naive bayes E Frank, M Hall, B Pfahringer Proceedings of the Nineteenth conference on Uncertainty in Artificial …, 2002	369	2002
WEKAâˆ’Experiences with a Java Open-Source Project RR Bouckaert, E Frank, MA Hall, G Holmes, B Pfahringer, P Reutemann, ... Journal of Machine Learning Research 11 (Sep), 2533-2541, 2010	358	2010
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	358	2008
Multinomial naive bayes for text categorization revisited AM Kibriya, E Frank, B Pfahringer, G Holmes Australasian Joint Conference on Artificial Intelligence, 488-499, 2004	329	2004
Winning the KDD99 classification cup: bagged boosting B Pfahringer ACM SIGKDD Explorations Newsletter 1 (2), 65-66, 2000	264	2000
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	246	2013
Leveraging bagging for evolving data streams A Bifet, G Holmes, B Pfahringer Joint European conference on machine learning and knowledge discovery in …, 2010	208	2010
Multiclass alternating decision trees G Holmes, B Pfahringer, R Kirkby, E Frank, M Hall European Conference on Machine Learning, 161-172, 2002	197	2002
A two-level learning method for generalized multi-instance problems N Weidmann, E Frank, B Pfahringer European Conference on Machine Learning, 468-479, 2003	172	2003
Meka: a multi-label/multi-target extension to weka J Read, P Reutemann, B Pfahringer, G Holmes The Journal of Machine Learning Research 17 (1), 667-671, 2016	171	2016
New options for hoeffding trees B Pfahringer, G Holmes, R Kirkby Australasian Joint Conference on Artificial Intelligence, 90-99, 2007	156	2007
Prediction of ordinal classes using regression trees S Kramer, G Widmer, B Pfahringer, M De Groeve Fundamenta Informaticae 47 (1-2), 1-13, 2001	155	2001
andI M Hall, E Frank, G Holmes, B Pfahringer, P Reutemann H. Witten, The weka data mining software: An update, SIGKDD Explor. Newsl 11 …, 2009	142	2009

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