Bernhard Schölkopf

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Alex SmolaBoson AIE-mail confirmado em smola.org
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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyE-mail confirmado em tu-berlin.de
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Kun ZhangCarnegie Mellon University & Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)E-mail confirmado em cmu.edu
Robert C. WilliamsonUniversity of TübingenE-mail confirmado em uni-tuebingen.de
Michael HirschMax Planck Institute for Intelligent Systems TübingenE-mail confirmado em tuebingen.mpg.de
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Jeremy HillNational Center for Adaptive Neurotechnologies; Stratton VA Medical Center, Albany, NY, USAE-mail confirmado em med.cornell.edu

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Bernhard Schölkopf

Director, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETH

E-mail confirmado em tuebingen.mpg.de

Machine Learning Causal Inference Artificial Intelligence Computational Photography Statistics


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
Learning with kernels: support vector machines, regularization, optimization, and beyond B Schölkopf, AJ Smola MIT press, 2002	25174*	2002
A tutorial on support vector regression AJ Smola, B Schölkopf Statistics and computing 14, 199-222, 2004	16070	2004
Nonlinear component analysis as a kernel eigenvalue problem B Schölkopf, A Smola, KR Müller Neural computation 10 (5), 1299-1319, 1998	11173	1998
Semi-supervised learning O Chapelle, B Schölkopf, A Zien MIT Press, 2006	8025*	2006
Estimating the support of a high-dimensional distribution B Schölkopf, JC Platt, J Shawe-Taylor, AJ Smola, RC Williamson Neural computation 13 (7), 1443-1471, 2001	7656	2001
Support vector machines MA Hearst, ST Dumais, E Osuna, J Platt, B Scholkopf IEEE Intelligent Systems and their applications 13 (4), 18-28, 1998	7117	1998
A kernel two-sample test A Gretton, KM Borgwardt, MJ Rasch, B Schölkopf, A Smola The Journal of Machine Learning Research 13 (1), 723-773, 2012	6330	2012
Learning with local and global consistency D Zhou, O Bousquet, T Lal, J Weston, B Schölkopf Advances in neural information processing systems 16, 2003	5635	2003
An introduction to kernel-based learning algorithms KR Muller, S Mika, G Ratsch, K Tsuda, B Scholkopf IEEE Transactions on Neural Networks 12 (2), 181-201, 2001	5064*	2001
Advances in kernel methods: support vector learning B Schölkopf, A Burges, C.J.C.: Smola The MIT press, 1999	4456*	1999
Kernel principal component analysis B Schölkopf, A Smola, KR Müller International conference on artificial neural networks, 583-588, 1997	3853	1997
Fisher discriminant analysis with kernels S Mika, G Rätsch, J Weston, B Schölkopf, K Müller Neural networks for signal processing IX, 1999	3840	1999
New support vector algorithms B Schölkopf, AJ Smola, RC Williamson, PL Bartlett Neural computation 12 (5), 1207-1245, 2000	3619	2000
Support vector method for novelty detection B Schölkopf, RC Williamson, A Smola, J Shawe-Taylor, J Platt Advances in neural information processing systems 12, 1999	3283	1999
Kernel methods in machine learning T Hofmann, B Schölkopf, AJ Smola Annals of Statistics 36 (3), 1171-1220, 2008	3019	2008
A gene expression map of Arabidopsis thaliana development M Schmid, TS Davison, SR Henz, UJ Pape, M Demar, M Vingron, ... Nature genetics 37 (5), 501-506, 2005	2823	2005
A kernel method for the two-sample-problem A Gretton, K Borgwardt, M Rasch, B Schölkopf, A Smola Advances in neural information processing systems 19, 2006	2801	2006
Elements of causal inference: foundations and learning algorithms J Peters, D Janzing, B Schölkopf The MIT Press, 2017	2444	2017
A generalized representer theorem B Schölkopf, R Herbrich, AJ Smola International conference on computational learning theory, 416-426, 2001	2391	2001
Correcting sample selection bias by unlabeled data J Huang, A Gretton, K Borgwardt, B Schölkopf, A Smola Advances in neural information processing systems 19, 2006	2264	2006

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