S. Mika

发表

Input space versus feature space in kernel-based methods

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, IEEE Trans. Neural Networks.

R. C. Williamson, B. Schölkopf, Alex Smola, 2001 .

Predicting Lipophilicity of Drug‐Discovery Molecules using Gaussian Process Models

K. Müller, S. Mika, Anton Schwaighofer, 2007, ChemMedChem.

Fisher discriminant analysis with kernels

B. Scholkopf, S. Mika, G. Ratsch, 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

Quantization Functionals and Regularized Principal Manifolds

Alexander J. Smola, B. Schölkopf, S. Mika, 1998 .

Kernel PCA and De-Noising in Feature Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1998, NIPS.

A kernel view of the dimensionality reduction of manifolds

Bernhard Schölkopf, Daniel D. Lee, Sebastian Mika, 2004, ICML.

Machine learning models for lipophilicity and their domain of applicability.

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2007, Molecular pharmaceutics.

Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

Klaus-Robert Müller, Sebastian Mika, Thomas Steger-Hartmann, 2009, J. Chem. Inf. Model..

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2007, J. Comput. Aided Mol. Des..

Accurate Solubility Prediction with Error Bars for Electrolytes: A Machine Learning Approach

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2007, J. Chem. Inf. Model..

A Probabilistic Approach to Classifying Metabolic Stability

Klaus-Robert Müller, Sebastian Mika, Anton Schwaighofer, 2008, J. Chem. Inf. Model..

Kernel PCA pattern reconstruction via approximate pre-images.

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1998 .

Invariant Feature Extraction and Classification in Kernel Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 1999, NIPS.

Kernels, Pre-images and Optimization

Klaus-Robert Müller, Sebastian Mika, John C. Snyder, 2013, Empirical Inference.

Classifying 'Drug-likeness' with Kernel-Based Learning Methods

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2005, J. Chem. Inf. Model..

Kernel peA and DeNoising in Feature Spaces

Sebastian Mika, Rudower Chaussee, Matthias Scholz, 1998 .

Quantization Functionals andRegularized Principal

S. Mika, ManifoldsAlex J. Smola, 1998 .

Robust Ensemble Learning for Data Mining

B. Schölkopf, K. Müller, Alex Smola, 2000, PAKDD.

Learning to Predi t the Leave { one { out Error ofKernel Based Classi

S. Mika, Klaus-Robert M uller, ersKoji, 2007 .

How wrong can we get? A review of machine learning approaches and error bars.

Gilles Blanchard, Sebastian Mika, Anton Schwaighofer, 2009, Combinatorial chemistry & high throughput screening.

Kernel Fisher Discriminants

Sebastian Mika, S. Mika, 2003 .

An introduction to kernel-based learning algorithms

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2001, IEEE Trans. Neural Networks.

Constructing Descriptive and Discriminative Nonlinear Features: Rayleigh Coefficients in Kernel Feature Spaces

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

On the Convergence of Leveraging

Gunnar Rätsch, Sebastian Mika, Manfred K. Warmuth, 2001, NIPS.

Barrier Boosting

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2000, COLT.

A Mathematical Programming Approach to the Kernel Fisher Algorithm

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2000, NIPS.

Regularized Principal Manifolds

Bernhard Schölkopf, Alexander J. Smola, Sebastian Mika, 1999, J. Mach. Learn. Res..

Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 2000, German Conference on Bioinformatics.

Adapting Codes and Embeddings for Polychotomies

Gunnar Rätsch, Alexander J. Smola, Sebastian Mika, 2002, NIPS.

Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification

Gunnar Rätsch, Bernhard Schölkopf, Klaus-Robert Müller, 2002, IEEE Trans. Pattern Anal. Mach. Intell..

Bias-Correction of Regression Models: A Case Study on hERG Inhibition

Sebastian Mika, Fabian Rathke, Thomas Fox, 2009, J. Chem. Inf. Model..

Learning to Predict the Leave-One-Out Error of Kernel Based Classifiers

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2001, ICANN.

A Mathematical Programming Approach to the Kernel Fisher Algorithm

K. Müller, S. Mika, G. Rätsch, 2000, NIPS.

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

K. Müller, S. Mika, T. Schroeter, 2007, Journal of computer-aided molecular design.

An Introduction to Kernel-Based Learning Algorithms

S. Mika, Koji Tsuda, Klaus-Robert Müller, 2018, Handbook of Neural Network Signal Processing.