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Title:The Bayesian Backfitting Relevance Vector Machine
Authors: Aaron D'Souza ; Sethu Vijayakumar ; Stefan Schaal
Date:Jul 2004
Publication Title:Proc. of International Conference on Machine Learning (ICML 2004)
Publisher:ACM Press, New York, NY, USA
Publication Type:Conference Paper Publication Status:Published
Volume No:69 Page Nos:31
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 efficient 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 efficient algorithm using a Bayesian version of backfitting at its core. As we demonstrate on several regression and classification benchmarks, Bayesian backfitting offers a compelling alternative to current regression methods, especially when the size and dimensionality of the data challenge computational resources.
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Bibtex format
author = { Aaron D'Souza and Sethu Vijayakumar and Stefan Schaal },
title = {The Bayesian Backfitting Relevance Vector Machine},
book title = {Proc. of International Conference on Machine Learning (ICML 2004)},
publisher = {ACM Press, New York, NY, USA},
year = 2004,
month = {Jul},
volume = {69},
pages = {31},
doi = {},
url = {},

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