Sebastian Mika

发表

A Mathematical Programming Approach to the Kernel Fisher Algorithm

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

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

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

Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites

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

An introduction to kernel-based learning algorithms

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

Kernels, Pre-images and Optimization

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

v-Arc: Ensemble Learning in the Presence of Outliers

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

Predicting Error Bars for QSAR Models

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

Invariant Feature Extraction and Classification in Kernel Spaces

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

Regularized Principal Manifolds

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

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..

Kernel Fisher Discriminants

Sebastian Mika, S. Mika, 2003 .

Kern Fisher Diskriminaten

Sebastian Mika, 2002 .

A Probabilistic Approach to Classifying Metabolic Stability

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

An improved training algorithm for kernel Fisher discriminants

Bernhard Schölkopf, Alexander J. Smola, Sebastian Mika, 2001, AISTATS.

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..

Input space versus feature space in kernel-based methods

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

Robust Ensemble Learning for Data Mining

Gunnar Rätsch, Bernhard Schölkopf, Alexander J. Smola, 2000, PAKDD.

Benchmark Data Set for in Silico Prediction of Ames Mutagenicity

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

Kern Fisher Diskriminanten

Sebastian Mika, 2002, Ausgezeichnete Informatikdissertationen.

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.

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

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

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.

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

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

Barrier Boosting

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

On the Convergence of Leveraging

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

Adapting Codes and Embeddings for Polychotomies

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

Machine learning models for lipophilicity and their domain of applicability.

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

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

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

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..

Kernel PCA pattern reconstruction via approximate pre-images.

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

Meta Learning : Learning to Predict the Leave – one – out Error

Gunnar Rätsch, Klaus-Robert Müller, Sebastian Mika, 2007 .

Kernel peA and DeNoising in Feature Spaces

Sebastian Mika, Rudower Chaussee, Matthias Scholz, 1998 .