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Bernhard Pfahringer
Bernhard Pfahringer
Professor of Computer Science, University of Waikato
Geverifieerd e-mailadres voor waikato.ac.nz - Homepage
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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
241172009
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
19582010
Classifier chains for multi-label classification
J Read, B Pfahringer, G Holmes, E Frank
Machine learning 85 (3), 333-359, 2011
19462011
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
9122009
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
7412009
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
7032009
Multinomial naive bayes for text categorization revisited
AM Kibriya, E Frank, B Pfahringer, G Holmes
Australasian Joint Conference on Artificial Intelligence, 488-499, 2004
5312004
Meta-Learning by Landmarking Various Learning Algorithms.
B Pfahringer, H Bensusan, CG Giraud-Carrier
ICML, 743-750, 2000
5122000
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
5112008
Locally weighted naive bayes
E Frank, M Hall, B Pfahringer
arXiv preprint arXiv:1212.2487, 2012
4642012
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
4242010
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
3682013
Leveraging bagging for evolving data streams
A Bifet, G Holmes, B Pfahringer
Joint European conference on machine learning and knowledge discovery in …, 2010
3632010
Winning the KDD99 classification cup: Bagged boosting
B Pfahringer
ACM SIGKDD Explorations Newsletter 1 (2), 65-66, 2000
3122000
Regularisation of neural networks by enforcing lipschitz continuity
H Gouk, E Frank, B Pfahringer, MJ Cree
Machine Learning 110 (2), 393-416, 2021
2912021
Meka: a multi-label/multi-target extension to weka
J Read, P Reutemann, B Pfahringer, G Holmes
2832016
Multiclass alternating decision trees
G Holmes, B Pfahringer, R Kirkby, E Frank, M Hall
European Conference on Machine Learning, 161-172, 2002
2422002
Machine learning for data streams: with practical examples in MOA
A Bifet, R Gavalda, G Holmes, B Pfahringer
MIT press, 2018
2202018
A two-level learning method for generalized multi-instance problems
N Weidmann, E Frank, B Pfahringer
European Conference on Machine Learning, 468-479, 2003
2122003
New options for hoeffding trees
B Pfahringer, G Holmes, R Kirkby
Australasian Joint Conference on Artificial Intelligence, 90-99, 2007
2062007
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Artikelen 1–20