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5 results (View BibTeX file of all listed publications)

2000

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The Kernel Trick for Distances

Schölkopf, B.

(MSR-TR-2000-51), Microsoft Research, Redmond, WA, USA, 2000 (techreport)

Abstract
A method is described which, like the kernel trick in support vector machines (SVMs), lets us generalize distance-based algorithms to operate in feature spaces, usually nonlinearly related to the input space. This is done by identifying a class of kernels which can be represented as normbased distances in Hilbert spaces. It turns out that common kernel algorithms, such as SVMs and kernel PCA, are actually really distance based algorithms and can be run with that class of kernels, too. As well as providing a useful new insight into how these algorithms work, the present work can form the basis for conceiving new algorithms.

ei

PDF Web [BibTex]

2000

ei Schölkopf, B. The Kernel Trick for Distances (MSR-TR-2000-51), Microsoft Research, Redmond, WA, USA, 2000 (techreport)

PDF Web [BibTex]

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Kernel method for percentile feature extraction

Schölkopf, B., Platt, J., Smola, A.

(MSR-TR-2000-22), Microsoft Research, 2000 (techreport)

Abstract
A method is proposed which computes a direction in a dataset such that a speci􏰘ed fraction of a particular class of all examples is separated from the overall mean by a maximal margin􏰤 The pro jector onto that direction can be used for class􏰣speci􏰘c feature extraction􏰤 The algorithm is carried out in a feature space associated with a support vector kernel function􏰢 hence it can be used to construct a large class of nonlinear fea􏰣 ture extractors􏰤 In the particular case where there exists only one class􏰢 the method can be thought of as a robust form of principal component analysis􏰢 where instead of variance we maximize percentile thresholds􏰤 Fi􏰣 nally􏰢 we generalize it to also include the possibility of specifying negative examples􏰤

ei

PDF [BibTex]

ei Schölkopf, B., Platt, J., Smola, A. Kernel method for percentile feature extraction (MSR-TR-2000-22), Microsoft Research, 2000 (techreport)

PDF [BibTex]

1999

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Estimating the support of a high-dimensional distribution

Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R.

(MSR-TR-99-87), Microsoft Research, 1999 (techreport)

ei

Web [BibTex]

1999

ei Schölkopf, B., Platt, J., Shawe-Taylor, J., Smola, A., Williamson, R. Estimating the support of a high-dimensional distribution (MSR-TR-99-87), Microsoft Research, 1999 (techreport)

Web [BibTex]

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Generalization Bounds via Eigenvalues of the Gram matrix

Schölkopf, B., Shawe-Taylor, J., Smola, A., Williamson, R.

(99-035), NeuroCOLT, 1999 (techreport)

ei

[BibTex]

ei Schölkopf, B., Shawe-Taylor, J., Smola, A., Williamson, R. Generalization Bounds via Eigenvalues of the Gram matrix (99-035), NeuroCOLT, 1999 (techreport)

[BibTex]

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Sparse kernel feature analysis

Smola, A., Mangasarian, O., Schölkopf, B.

(99-04), Data Mining Institute, 1999, 24th Annual Conference of Gesellschaft f{\"u}r Klassifikation, University of Passau (techreport)

ei

PostScript [BibTex]

ei Smola, A., Mangasarian, O., Schölkopf, B. Sparse kernel feature analysis (99-04), Data Mining Institute, 1999, 24th Annual Conference of Gesellschaft f{\"u}r Klassifikation, University of Passau (techreport)

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