Sebastian Mika

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Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Alex SmolaAmazon Web ServicesVerified email at smola.org
Koji TsudaProfessor, GSFS, The University of TokyoVerified email at k.u-tokyo.ac.jp
Jason WestonFacebookVerified email at fb.com
Matthias ScholzEdmund Mach FoundationVerified email at fmach.it
Robert C. WilliamsonUniversity of TübingenVerified email at uni-tuebingen.de
Soeren SonnenburgMachine Learning ResearcherVerified email at tomtom.com
Gilles BlanchardProfessor of Statistics, University of Paris-SaclayVerified email at universite-paris-saclay.fr
Fabian RathkeCARIADVerified email at cariad.technology
John C. SnyderFormer Postdoc, Technische Universität Berlin / Max-Planck-Institut fürVerified email at uci.edu
Pavel LaskovUniversity of LiechtensteinVerified email at uni.li
David TaxAssistant professor, Delft University of TechnologyVerified email at tudelft.nl
Konrad RieckProfessor of Computer Science, TU BraunschweigVerified email at tu-bs.de
Christin Schäferacs plus GmbHVerified email at acs-plus.de
Paul von Bünauidalab GmbHVerified email at idalab.de
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyVerified email at cs.berkeley.edu
vapnikProfessor of Columbia, Fellow of NEC Labs America, Verified email at nec-labs.com
Stephane MallatProfesseur de Mathématiques Appliquées et Informatique, École Normale SupérieureVerified email at ens.fr

Sebastian Mika

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data analysis big data datas science machine learning statistics


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Fisher discriminant analysis with kernels S Mika, G Ratsch, J Weston, B Scholkopf, KR Mullers Neural networks for signal processing IX: Proceedings of the 1999 IEEE …, 1999	3580	1999
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	2847	2001
An introduction to kernel-based learning algorithms. KR Müller, S Mika, G Rätsch, K Tsuda, B Schölkopf IEEE Trans. neural networks 12 (2), 181-201, 2001	2122	2001
Input space versus feature space in kernel-based methods B Scholkopf, S Mika, CJC Burges, P Knirsch, KR Muller, G Ratsch, ... IEEE transactions on neural networks 10 (5), 1000-1017, 1999	1593	1999
Kernel PCA and De-noising in feature spaces. S Mika, B Schölkopf, AJ Smola, KR Müller, M Scholz, G Rätsch NIPS 11, 536-542, 1998	1260	1998
A kernel view of the dimensionality reduction of manifolds J Ham, DD Lee, S Mika, B Schölkopf Proceedings of the twenty-first international conference on Machine learning, 47, 2004	640	2004
Engineering support vector machine kernels that recognize translation initiation sites A Zien, G Rätsch, S Mika, B Schölkopf, T Lengauer, KR Müller Bioinformatics 16 (9), 799-807, 2000	622	2000
Constructing boosting algorithms from SVMs: An application to one-class classification G Ratsch, S Mika, B Scholkopf, KR Muller IEEE Transactions on Pattern Analysis and Machine Intelligence 24 (9), 1184-1199, 2002	308	2002
Benchmark data set for in silico prediction of Ames mutagenicity K Hansen, S Mika, T Schroeter, A Sutter, A Ter Laak, T Steger-Hartmann, ... Journal of chemical information and modeling 49 (9), 2077-2081, 2009	271	2009
A mathematical programming approach to the kernel fisher algorithm S Mika, G Rätsch, KR Müller Advances in neural information processing systems, 591-597, 2001	250	2001
Invariant feature extraction and classification in kernel spaces. S Mika, G Rätsch, J Weston, B Schölkopf, AJ Smola, KR Müller NIPS, 526-532, 1999	250	1999
Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces S Mika, G Ratsch, J Weston, B Scholkopf, A Smola, KR Muller IEEE Transactions on Pattern Analysis and Machine Intelligence 25 (5), 623-628, 2003	246	2003
Kernel fisher discriminants S Mika	178	2003
Kernel PCA Pattern Reconstruction via Approximate Pre-Images B Schölkopf, S Mika, A Smola, G Rätsch, KR Müller International Conference on Artificial Neural Networks, 147-152, 1998	155	1998
An improved training algorithm for kernel fisher discriminants S Mika, AJ Smola, B Schölkopf International Workshop on Artificial Intelligence and Statistics, 209-215, 2001	141	2001
Regularized principal manifolds A Smola, S Mika, B Schölkopf, R Williamson MIT Press, 2001	109	2001
Classifying ‘drug-likeness' with kernel-based learning methods KR Müller, G Rätsch, S Sonnenburg, S Mika, M Grimm, N Heinrich Journal of chemical information and modeling 45 (2), 249-253, 2005	105	2005
Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules TS Schroeter, A Schwaighofer, S Mika, A Ter Laak, D Suelzle, U Ganzer, ... Journal of Computer-aided molecular design 21 (9), 485-498, 2007	76	2007
Accurate solubility prediction with error bars for electrolytes: A machine learning approach A Schwaighofer, T Schroeter, S Mika, J Laub, A Ter Laak, D Sülzle, ... Journal of chemical information and modeling 47 (2), 407-424, 2007	74	2007
Robust ensemble learning G Rätsch, B Schölkopf, A Smola, S Mika, T Onoda, KR Muller Advances in large margin classifiers, 2000	74	2000

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