ML Anthology

The Relevance Vector Machine

Michael E. Tipping
NeurIPS 1999 pp. 652-658
/neurips/1999/tipping1999neurips-relevance/

Abstract

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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Text

Tipping. "The Relevance Vector Machine." Neural Information Processing Systems, 1999.

Markdown

[Tipping. "The Relevance Vector Machine." Neural Information Processing Systems, 1999.](https://mlanthology.org/neurips/1999/tipping1999neurips-relevance/)

BibTeX

@inproceedings{tipping1999neurips-relevance,
  title     = {{The Relevance Vector Machine}},
  author    = {Tipping, Michael E.},
  booktitle = {Neural Information Processing Systems},
  year      = {1999},
  pages     = {652-658},
  url       = {https://mlanthology.org/neurips/1999/tipping1999neurips-relevance/}
}