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M.E. Tipping (2000)

The Relevance Vector Machine

In: Advances in Neural Information Processing Systems 12, ed. by Sara A Solla and Todd K Leen and Klaus-Robert M\"uller, Cambridge, Mass: MIT Press.

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 probabilistic 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 treatment 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 performance, the RVM requires dramatically fewer kernel functions.

by admin last modified 2007-01-31 11:08

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