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  Healing the Relevance Vector Machine through Augmentation

Rasmussen, C., & 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/21.11116/0000-0005-0E0C-0
Genre: Conference Paper

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https://dl.acm.org/citation.cfm?doid=1102351.1102438 (Publisher version)
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 Creators:
Rasmussen, CE1, 2, Author           
Candela, JQ3, Author           
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Friedrich Miescher Laboratory, Max Planck Society, Max-Planck-Ring 9, 72076 Tübingen, DE, ou_2575692              

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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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 Dates: 2005-08
 Publication Status: Issued
 Pages: -
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 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:
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Publ. Info: New York, NY, USA : ACM Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 689 - 696 Identifier: ISBN: 1-59593-180-5