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  Healing the Relevance Vector Machine through Augmentation

Rasmussen, C., & Quiñonero-Candela, J. (2005). Healing the Relevance Vector Machine through Augmentation. In S. Dzeroski, L. de Raedt, & S. Wrobel (Eds.), ICML '05: 22nd international conference on Machine learning (pp. 689-696). New York, NY, USA: ACM Press.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D6D9-C Version Permalink: https://hdl.handle.net/21.11116/0000-0010-5A4A-7
Genre: Conference Paper

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 Creators:
Rasmussen, CE1, Author           
Quiñonero-Candela, J, Author                 
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

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 Abstract: The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties have the unintuitive property, that emphthey get smaller the further you move away from the training cases. We give a thorough analysis. Inspired by the analogy to non-degenerate Gaussian Processes, we suggest augmentation to solve the problem. The purpose of the resulting model, RVM*, is primarily to corroborate the theoretical and experimental analysis. Although RVM* could be used in practical applications, it is no longer a truly sparse model. Experiments show that sparsity comes at the expense of worse predictive distributions.

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Language(s):
 Dates: 2005-08
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: BibTex Citekey: 3460
DOI: 10.1145/1102351.1102438
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Title: 22nd International Conference on Machine Learning (ICML 2005)
Place of Event: Bonn, Germany
Start-/End Date: 2005-08-07 - 2005-08-11

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Title: ICML '05: 22nd international conference on Machine learning
Source Genre: Proceedings
 Creator(s):
Dzeroski, S, Editor
de Raedt, L, Editor
Wrobel, S, Editor
Affiliations:
-
Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 689 - 696 Identifier: ISBN: 1-59593-180-5