Ulf Brefeld

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Marius KloftTU KaiserslauternVerified email at cs.uni-kl.de
Soeren SonnenburgMachine Learning ResearcherVerified email at tomtom.com
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & Max Planck Institute for Informatics, GermanyVerified email at tu-berlin.de
Nico GoernitzCTO @ MorphAIs | former PostDoc @ TU-BerlinVerified email at tu-berlin.de
Konrad RieckTechnische Universität BerlinVerified email at tu-berlin.de
Maryam TavakolEindhoven University of TechnologyVerified email at tue.nl
Alexander BinderAssociate Professor, Singapore Institute of Technology (SIT) and University of Oslo (UiO)Verified email at singaporetech.edu.sg
Pavel LaskovUniversity of LiechtensteinVerified email at uni.li
Shinichi NakajimaMachine Learning Group, Technische Universität BerlinVerified email at tu-berlin.de
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Thomas GärtnerTU Wien (Technical University of Vienna)Verified email at tuwien.ac.at
Eraldo R. FernandesSoundHoundVerified email at soundhound.com
Steffen BickelNokia Gate5 GmbHVerified email at cs.uni-potsdam.de
Jörg HakenbergIllumina, Inc.Verified email at illumina.com
Ulf LeserKnowledge Management in Bioinformatics, Humboldt-Universität zu BerlinVerified email at informatik.hu-berlin.de
Tammo KruegerTU BerlinVerified email at tu-berlin.de
Roi BlancoAmazonVerified email at udc.es
Jamal Abdul NasirAssistant Professor, School of Computer Science, University of Galway, IrelandVerified email at universityofgalway.ie
Luca ChiarandiniGoogle IncVerified email at google.com
Patrick DuesselViacomCBSVerified email at riskbeam.io

Ulf Brefeld

Leuphana Universität Lüneburg

Verified email at leuphana.de - Homepage

Machine Learning


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l_p-Norm Multiple Kernel Learning M Kloft, U Brefeld, S Sonnenburg, A Zien The Journal of Machine Learning Research 12, 953-997, 2011	445	2011
Toward supervised anomaly detection N Görnitz, M Kloft, K Rieck, U Brefeld Journal of Artificial Intelligence Research 46, 235-262, 2013	416	2013
Efficient and accurate lp-norm multiple kernel learning M Kloft, U Brefeld, P Laskov, KR Müller, A Zien, S Sonnenburg Advances in neural information processing systems 22, 2009	347	2009
Efficient co-regularised least squares regression U Brefeld, T Gärtner, T Scheffer, S Wrobel Proceedings of the 23rd international conference on Machine learning, 137-144, 2006	231	2006
Co-EM support vector learning U Brefeld, T Scheffer Proceedings of the twenty-first international conference on Machine learning, 16, 2004	225	2004
AUC maximizing support vector learning U Brefeld, T Scheffer Proceedings of the ICML 2005 workshop on ROC Analysis in Machine Learning, 2005	184	2005
Non-sparse regularization and efficient training with multiple kernels M Kloft, U Brefeld, S Sonnenburg, A Zien arXiv preprint arXiv:1003.0079 186, 189-190, 2010	152	2010
Support vector machines with example dependent costs U Brefeld, P Geibel, F Wysotzki Machine Learning: ECML 2003: 14th European Conference on Machine Learning …, 2003	104	2003
Semi-supervised learning for structured output variables U Brefeld, T Scheffer Proceedings of the 23rd international conference on Machine learning, 145-152, 2006	99	2006
Active learning for network intrusion detection N Görnitz, M Kloft, K Rieck, U Brefeld Proceedings of the 2nd ACM workshop on Security and artificial intelligence …, 2009	93	2009
Factored MDPs for detecting topics of user sessions M Tavakol, U Brefeld Proceedings of the 8th ACM Conference on Recommender Systems, 33-40, 2014	76	2014
Automatic feature selection for anomaly detection M Kloft, U Brefeld, P Düessel, C Gehl, P Laskov Proceedings of the 1st ACM workshop on Workshop on AISec, 71-76, 2008	72	2008
Multi-view discriminative sequential learning U Brefeld, C Büscher, T Scheffer Machine Learning: ECML 2005: 16th European Conference on Machine Learning …, 2005	72	2005
Transductive support vector machines for structured variables A Zien, U Brefeld, T Scheffer Proceedings of the 24th international conference on Machine learning, 1183-1190, 2007	56	2007
Spatio-temporal convolution kernels K Knauf, D Memmert, U Brefeld Machine learning 102, 247-273, 2016	54	2016
Systematic feature evaluation for gene name recognition J Hakenberg, S Bickel, C Plake, U Brefeld, H Zahn, L Faulstich, U Leser, ... BMC bioinformatics 6 (1), 1-11, 2005	50	2005
Learning to rate player positioning in soccer U Dick, U Brefeld Big data 7 (1), 71-82, 2019	45	2019
Probabilistic movement models and zones of control U Brefeld, J Lasek, S Mair Machine Learning 108 (1), 127-147, 2019	45	2019
Hybrid models for future event prediction G Amodeo, R Blanco, U Brefeld Proceedings of the 20th ACM international conference on Information and …, 2011	45	2011
Approximate Tree Kernels. K Rieck, T Krueger, U Brefeld, KR Müller Journal of Machine Learning Research 11 (2), 2010	44	2010

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