Sebastian Mika - Google Scholar

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	All	Since 2015
Citations	14186	3820
h-index	29	16
i10-index	38	21

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Co-authors

Klaus-Robert MüllerGoogle Brain (on leave of Professor for Machine Learning, TU Berlin & Korea University, Seoul)Verified 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. WilliamsonProfessor of Computer Science, Australian National University; Distinguished Researcher, DATA61Verified email at anu.edu.au
Soeren SonnenburgMachine Learning ResearcherVerified email at tomtom.com
Manfred K. WarmuthProfessor of Computer Science, University of California at Santa CruzVerified email at ucsc.edu
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
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

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 Muller, S Mika, G Ratsch, K Tsuda, B Scholkopf IEEE transactions on neural networks 12 (2), 181-201, 2001	4325	2001
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	3274	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	1506	1999
Kernel PCA and de-noising in feature spaces S Mika, B Schölkopf, AJ Smola, KR Müller, M Scholz, G Rätsch Advances in neural information processing systems, 536-542, 1999	1165	1999
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	607	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	605	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	287	2002
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	243	2003
Invariant feature extraction and classification in kernel spaces S Mika, G Rätsch, J Weston, B Schölkopf, AJ Smola, KR Müller Advances in neural information processing systems, 526-532, 2000	242	2000
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	235	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	232	2001
Kernel fisher discriminants S Mika	170	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	144	1998
An improved training algorithm for kernel Fisher discriminants. S Mika, AJ Smola, B Schölkopf AISTATS, 98-104, 2001	135	2001
Regularized principal manifolds AJ Smola, S Mika, B Schölkopf, RC Williamson Journal of Machine Learning Research 1 (Jun), 179-209, 2001	105	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	100	2005
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	70	2007
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	64	2007
Robust ensemble learning G Rätsch, B Schölkopf, A Smola, S Mika, T Onoda, KR Muller Advances in large margin classifiers, 2000	64	2000
Regularizing adaboost G Rätsch, T Onoda, KR Müller Advances in neural information processing systems, 564-570, 1999	60	1999

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