Sebastian Mika

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Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified email at tu-berlin.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
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
Pavel LaskovUniversity of LiechtensteinVerified email at uni.li
David TaxAssistant professor, Delft University of TechnologyVerified email at tudelft.nl
Christin Schäferacs plus GmbHVerified email at acs-plus.de
Gilles BlanchardProfessor of Statistics, University of Paris-SaclayVerified email at math.u-psud.fr
Fabian RathkeHella AglaiaVerified email at Hella.com
John C. SnyderFormer Postdoc, Technische Universität Berlin / Max-Planck-Institut fürVerified email at uci.edu
Klaus R. PawelzikUniversity Bremen, Germany.Verified email at neuro.uni-bremen.de
Konrad RieckProfessor of Computer Science, TU BraunschweigVerified email at tu-bs.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

Sebastian Mika

comtravo

Verified email at comtravo.com

data analysis big data datas science machine learning statistics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
An introduction to kernel-based learning algorithms KR Müller, S Mika, K Tsuda, K Schölkopf Handbook of Neural Network Signal Processing, 4-1-4-40, 2018	4517	2018
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	3486	1999
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	1570	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	1236	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	627	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	621	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	302	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	253	2009
Invariant feature extraction and classification in kernel spaces. S Mika, G Rätsch, J Weston, B Schölkopf, AJ Smola, KR Müller NIPS, 526-532, 1999	248	1999
A mathematical programming approach to the kernel fisher algorithm S Mika, G Rätsch, KR Müller Advances in neural information processing systems, 591-597, 2001	245	2001
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	242	2003
Kernel fisher discriminants S Mika	172	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 AISTATS, 98-104, 2001	138	2001
Regularized principal manifolds A Smola, S Mika, B Schölkopf, R Williamson MIT Press, 2001	108	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	102	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	72	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	72	2007
Robust ensemble learning G Rätsch, B Schölkopf, A Smola, S Mika, T Onoda, KR Muller Advances in large margin classifiers, 2000	70	2000
Regularizing adaboost G Rätsch, T Onoda, KR Müller Advances in neural information processing systems 11, 564-570, 1998	63	1998

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