Kernel-dependent support vector error bounds

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

doi:10.1049/cp:19991092

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Kernel-dependent support vector error bounds

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https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=819549
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Schölkopf, B., Shawe-Taylor, J., Smola, A., & Williamson, R. (1999). Kernel-dependent support vector error bounds. In Ninth International Conference on Artificial Neural Networks ICANN 99 (pp. 103-108). London, UK: Institute of Electrical Engineers.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-E761-2

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 eigenvalue distribution of the Gram matrix of the data. 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.