Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)
Michael Tipping
The support vector machine (SVM) is a state-of-the-art technique for regression and classification, combining excellent generalisation properties with a sparse kernel representation. However, it does suffer from a number of disadvantages, notably the absence of prob(cid:173) abilistic outputs, the requirement to estimate a trade-off parameter and the need to utilise 'Mercer' kernel functions. In this paper we introduce the Relevance Vector Machine (RVM), a Bayesian treat(cid:173) ment of a generalised linear model of identical functional form to the SVM. The RVM suffers from none of the above disadvantages, and examples demonstrate that for comparable generalisation per(cid:173) formance, the RVM requires dramatically fewer kernel functions.
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