Sparse Bayesian Learning and the Relevance Vector Machine

Sparse Bayesian Learning and the Relevance Vector Machine (2001)

by Michael E. Tipping , Alex Smola

Citations:

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Datum	Value	Source
TITLE	Sparse Bayesian Learning and the Relevance Vector Machine	SVM HeaderParse 0.1
AUTHOR NAME	Michael E. Tipping	user correction - Legacy Corrections
AUTHOR NAME	Alex Smola	user correction - Legacy Corrections
AUTHOR AFFIL	N	user correction - Legacy Corrections
ABSTRACT	This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vector machine' (RVM), a model of identical functional form to the popular and state-of-the-art `support vector machine' (SVM). We demonstrate that by exploiting a probabilistic Bayesian learning framework, we can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while oering a number of additional advantages. These include the benets of probabilistic predictions, automatic estimation of `nuisance' parameters, and the facility to utilise arbitrary basis functions (e.g. non-`Mercer' kernels).	user correction - Legacy Corrections
YEAR	2001	user correction - Legacy Corrections
CITATIONS	33 found	ParsCit 1.0