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- Klaus-Robert Müller, Sebastian Mika, Gunnar Rätsch, Koji Tsuda, Bernhard Schölkopf
- IEEE Trans. Neural Networks
- 2001

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods.… (More)

- Bernhard Schölkopf, Sebastian Mika, +4 authors Alexander J. Smola
- IEEE Trans. Neural Networks
- 1999

This paper collects some ideas targeted at advancing our understanding of the feature spaces associated with support vector (SV) kernel functions. We first discuss the geometry of feature space. In… (More)

Kernel PCA as a nonlinear feature extractor has proven powerful as a preprocessing step for classification algorithms. But it can also be considered as a natural generalization of linear principal… (More)

- Jihun Ham, Daniel D. Lee, Sebastian Mika, Bernhard Schölkopf
- ICML
- 2004

We interpret several well-known algorithms for dimensionality reduction of manifolds as kernel methods. Isomap, graph Laplacian eigenmap, and locally linear embedding (LLE) all utilize local… (More)

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

We investigate a new kernel–based classifier: the Kernel Fisher Discriminant (KFD). A mathematical programming formulation based on the observation that KFD maximizes the average marginpermits an… (More)

- Sebastian Mika
- 2003

In this thesis we consider statistical learning problems and machines. A statistical learning machine tries to infer rules from a given set of examples such that it is able to make correct… (More)

- Sebastian Mika, Gunnar Rätsch, Jason Weston, Bernhard Schölkopf, Alexander J. Smola, Klaus-Robert Müller
- IEEE Trans. Pattern Anal. Mach. Intell.
- 2003

We incorporate prior knowledge to construct nonlinear algorithms for invariant feature extraction and discrimination. Employing a unified framework in terms of a nonlinearized variant of the Rayleigh… (More)

- Alexander Zien, Gunnar Rätsch, Sebastian Mika, Bernhard Schölkopf, Thomas Lengauer, Klaus-Robert Müller
- Bioinformatics
- 1999

MOTIVATION
In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code for proteins. These points are called… (More)

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

We show via an equivalence of mathematical programs that a support vector (SV) algorithm can be translated into an equivalent boosting-like algorithm and vice versa. We exemplify this translation… (More)