Generalization Bounds via Eigenvalues of the Gram matrix

Schölkopf, B; Shawe-Taylor, J; Smola, AJ; Williamson, RC

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Generalization Bounds via Eigenvalues of the Gram matrix

Schölkopf, B., Shawe-Taylor, J., Smola, A., & Williamson, R.(1999). Generalization Bounds via Eigenvalues of the Gram matrix (NC2-TR-1999-035). London, UK: University of London: Royal Holloway College: NeuroCOLT 2.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-E77D-4 Version Permalink: https://hdl.handle.net/21.11116/0000-0005-C4ED-3

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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.41.349 (Any fulltext) Open Access status unknown

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Creators:
Schölkopf, B¹, Author
Shawe-Taylor, J, Author
Smola, AJ, Author
Williamson, RC, Author

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1External Organizations, ou_persistent22

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Abstract: Model selection in Support Vector machines is usually carried out by minimizing the quotient of the radius of the smallest enclosing sphere of the data and the observed margin on the training set. We provide a new criterion taking the distribution within that sphere into account by considering the Gram matrix of the data. In particular, this makes use of the eigenvalue distribution of the matrix. Experimental results on real world data show that this new criterion provides a good prediction of the shape of the curve relating generalization error to kernel width.

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Dates: Date issued: 1999-03

Publication Status: Issued

Pages: 15

Publishing info: London, UK : University of London: Royal Holloway College: NeuroCOLT 2

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Identifiers: Report Nr.: NC2-TR-1999-035
BibTex Citekey: 1821

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