Sebastian Mika - Google Scholar Citations

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Co-authors

Klaus-Robert MüllerProfessor for Machine Learning, TU Berlin, Germany and Korea University, Seoul, Korea and MPIIVerified email at tu-berlin.de
Gunnar RätschProfessor, ETH ZürichVerified email at inf.ethz.ch
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; and Distinguished Amazon ScholarVerified 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 PotsdamVerified email at math.uni-potsdam.de
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
Fabian RathkeHella AglaiaVerified email at Hella.com
Klaus R. PawelzikUniversity Bremen, Germany.Verified email at neuro.uni-bremen.de
John C. SnyderFormer Postdoc, Technische Universität Berlin / Max-Planck-Institut fürVerified email at uci.edu
Konrad RieckProfessor of Computer Science, TU BraunschweigVerified email at tu-bs.de
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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Verified email at comtravo.com

data analysis big data datas science machine learning statistics


Title	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	4135	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	3045	1999
Input space versus feature space in kernel-based methods B Schölkopf, S Mika, CJC Burges, P Knirsch, KR Müller, G Rätsch, ... IEEE transactions on neural networks 10 (5), 1000-1017, 1999	1463	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	1049	1999
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	593	2000
A kernel view of the dimensionality reduction of manifolds JH Ham, DD Lee, S Mika, B Schölkopf Departmental Papers (ESE), 93, 2004	571	2004
Constructing boosting algorithms from SVMs: An application to one-class classification G Rätsch, S Mika, B Schölkopf, KR Müller IEEE Transactions on Pattern Analysis & Machine Intelligence, 1184-1199, 2002	271	2002
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	239	2000
Constructing descriptive and discriminative nonlinear features: Rayleigh coefficients in kernel feature spaces S Mika, G Rätsch, J Weston, B Schölkopf, A Smola, KR Müller IEEE Transactions on Pattern Analysis & Machine Intelligence, 623-633, 2003	236	2003
A mathematical programming approach to the kernel fisher algorithm S Mika, G Rätsch, KR Müller Advances in neural information processing systems, 591-597, 2001	227	2001
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	215	2009
Kernel fisher discriminants S Mika	166	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	136	1998
An improved training algorithm for kernel Fisher discriminants. S Mika, AJ Smola, B Schölkopf AISTATS, 98-104, 2001	131	2001
Regularized principal manifolds AJ Smola, S Mika, B Schölkopf, RC Williamson Journal of Machine Learning Research 1 (Jun), 179-209, 2001	102	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	101	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	66	2007
Robust ensemble learning G Rätsch, B Schölkopf, AJ Smola, S Mika, T Onoda, KR Müller	63	2000
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	60	2007
Regularizing adaboost G Rätsch, T Onoda, KR Müller Advances in neural information processing systems, 564-570, 1999	56	1999

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