Document Detail


Bayesian support vector regression using a unified loss function.
MedLine Citation:
PMID:  15387245     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
In this paper, we use a unified loss function, called the soft insensitive loss function, for Bayesian support vector regression. We follow standard Gaussian processes for regression to set up the Bayesian framework, in which the unified loss function is used in the likelihood evaluation. Under this framework, the maximum a posteriori estimate of the function values corresponds to the solution of an extended support vector regression problem. The overall approach has the merits of support vector regression such as convex quadratic programming and sparsity in solution representation. It also has the advantages of Bayesian methods for model adaptation and error bars of its predictions. Experimental results on simulated and real-world data sets indicate that the approach works well even on large data sets.
Authors:
Wei Chu; S Sathiya Keerthi; Chong Jin Ong
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council     Volume:  15     ISSN:  1045-9227     ISO Abbreviation:  -     Publication Date:  2004 Jan 
Date Detail:
Created Date:  2004-09-24     Completed Date:  2004-10-25     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  101211035     Medline TA:  IEEE Trans Neural Netw     Country:  United States    
Other Details:
Languages:  eng     Pagination:  29-44     Citation Subset:  IM    
Affiliation:
Gatsby Computational Neuroscience Unit, University College London, London WC1N 3AR, U.K.
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MeSH Terms
Descriptor/Qualifier:
Bayes Theorem*
Regression Analysis*
Grant Support
ID/Acronym/Agency:
1 P01 GM63208/GM/NIGMS NIH HHS

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