Sebastian Mika

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Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyEmail yang diverifikasi di tu-berlin.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHEmail yang diverifikasi di tuebingen.mpg.de
Gunnar RätschProfessor, ETH ZürichEmail yang diverifikasi di inf.ethz.ch
Alex SmolaBoson AIEmail yang diverifikasi di smola.org
Koji TsudaProfessor, GSFS, The University of TokyoEmail yang diverifikasi di k.u-tokyo.ac.jp
Jason WestonMetaEmail yang diverifikasi di fb.com
Matthias ScholzEdmund Mach FoundationEmail yang diverifikasi di fmach.it
Robert C. WilliamsonUniversity of TübingenEmail yang diverifikasi di uni-tuebingen.de
Soeren SonnenburgMachine Learning ResearcherEmail yang diverifikasi di tomtom.com
Manfred K. Warmuthvisiting Google BrainEmail yang diverifikasi di google.com
Andreas ReichelBayer AGEmail yang diverifikasi di bayer.com
Gilles BlanchardProfessor of Statistics, University of Paris-SaclayEmail yang diverifikasi di universite-paris-saclay.fr
Fabian RathkeCARIADEmail yang diverifikasi di cariad.technology
John C. SnyderFormer Postdoc, Technische Universität Berlin / Max-Planck-Institut fürEmail yang diverifikasi di uci.edu
Pavel LaskovUniversity of LiechtensteinEmail yang diverifikasi di uni.li
David TaxAssistant professor, Delft University of TechnologyEmail yang diverifikasi di tudelft.nl
Konrad RieckTechnische Universität BerlinEmail yang diverifikasi di tu-berlin.de
Christin Schäferacs plus GmbHEmail yang diverifikasi di acs-plus.de
Paul von BünauManaging Director at idalab GmbHEmail yang diverifikasi di idalab.de
Peter BartlettProfessor, EECS and Statistics, UC BerkeleyEmail yang diverifikasi di cs.berkeley.edu

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data analysis big data datas science machine learning statistics


Judul Urutkan menurut kutipan Urutkan menurut tahun Urutkan menurut judul	Dikutip oleh Dikutip oleh	Tahun
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	5069	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	3844	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	1743	1999
Kernel PCA and de-noising in feature spaces S Mika, B Schölkopf, A Smola, KR Müller, M Scholz, G Rätsch Advances in neural information processing systems 11, 1998	1502	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	729	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	676	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	410	2009
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	334	2002
Invariant feature extraction and classification in kernel spaces S Mika, G Rätsch, J Weston, B Schölkopf, A Smola, KR Müller Advances in neural information processing systems 12, 1999	258	1999
A mathematical programming approach to the kernel fisher algorithm S Mika, G Rätsch, KR Müller Advances in neural information processing systems 13, 2000	253	2000
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	248	2003
Kernel fisher discriminants S Mika	190	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	167	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
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	123	2005
Regularized principal manifolds AJ Smola, S Mika, B Schölkopf, RC Williamson Journal of Machine Learning Research 1 (Jun), 179-209, 2001	123	2001
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, 485-498, 2007	109	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	99	2007
Advances in neural information processing systems S Mika, G Rätsch, B Weston, B Schölkopf, AJ Smola, K Müller Advances in neural information processing systems, 526-542, 2000	74	2000
Robust ensemble learning G Rätsch, B Schölkopf, AJ Smola, S Mika, T Onoda, KR Müller	66	2000

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