article · 2004
The Bayesian Backfitting Relevance Vector Machine
Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal
Cited by 0Abstract and details only
Abstract
Traditional non-parametric statistical learning techniques are often computationally attractive, but lack the same generalization and model selection abilities as state-of-the-art Bayesian algorithms which, however, are usually computationally prohibitive. This paper makes several important contributions that allow Bayesian learning to scale to more complex, real-world learning scenarios. Firstly, we show that backfitting --- a traditional non-parametric, yet highly e#cient regression tool --- can be derived in a novel formulation within an expectation maximization (EM) framework and thus can finally be given a probabilistic interpretation. Secondly, we show that the general framework of sparse Bayesian learning and in particular the relevance vector machine (RVM), can be derived as a highly e#cient algorithm using a Bayesian version of backfitting at its core. As we demonstrate on several regression and classification benchmarks, Bayesian backfitting o#ers a compelling alternative to current regression methods, especially when the size and dimensionality of the data challenge computational resources.
Cite this paper
D'Souza, A., Vijayakumar, S., & Schaal, S. (2004). The Bayesian Backfitting Relevance Vector Machine.
D'Souza, Aaron, et al. “The Bayesian Backfitting Relevance Vector Machine.” 2004.
D'Souza, Aaron, Sethu Vijayakumar, and Stefan Schaal. 2004. “The Bayesian Backfitting Relevance Vector Machine.”
D'Souza, A., Vijayakumar, S. and Schaal, S. (2004) ‘The Bayesian Backfitting Relevance Vector Machine’.
@article{dampapossouza2004bayesian,
title = {{The Bayesian Backfitting Relevance Vector Machine}},
author = {D'Souza, Aaron and Vijayakumar, Sethu and Schaal, Stefan},
year = {2004}
}Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.