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Probabilistic interpretations and Bayesian methods for support vector machines

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1 Author(s)
P. Sollich ; Dept. of Math., King's Coll., London, UK

Support vector machines (SVMs) can be interpreted as maximum a posteriori solutions to inference problems with Gaussian process priors and appropriate likelihood functions. Focusing on the case of classification, the author shows first that such an interpretation gives a clear intuitive meaning to SVM kernels, as covariance functions of GP priors; this can be used to guide the choice of kernel. Next, a probabilistic interpretation allows Bayesian methods to be used for SVMs. Using a local approximation of the posterior around its maximum (the standard SVM solution), he discusses how the evidence for a given kernel and noise parameter can be estimated, and how approximate error bars for the classification of test points can be calculated

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Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470)  (Volume:1 )

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