Sebastian Mika
Sebastian Mika
Verified email at
Cited by
Cited by
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
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
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
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
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
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
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
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
Invariant feature extraction and classification in kernel spaces.
S Mika, G Rätsch, J Weston, B Schölkopf, AJ Smola, KR Müller
NIPS, 526-532, 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
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
Kernel fisher discriminants
S Mika
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
An improved training algorithm for kernel Fisher discriminants.
S Mika, AJ Smola, B Schölkopf
AISTATS, 98-104, 2001
Regularized principal manifolds
A Smola, S Mika, B Schölkopf, R Williamson
MIT Press, 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
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
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
Robust ensemble learning
G Rätsch, B Schölkopf, A Smola, S Mika, T Onoda, KR Muller
Advances in large margin classifiers, 2000
Regularizing adaboost
G Rätsch, T Onoda, KR Müller
Advances in neural information processing systems 11, 564-570, 1998
The system can't perform the operation now. Try again later.
Articles 1–20