Paper Info
Title
Sparse Bayes Machines for Binary Classification
Abstract
In this paper we propose a sparse representation for the Bayes Machine based on the approach followed by the Informative Vector Machine (IVM). However, some extra modifications are included to guarantee a better approximation to the posterior distribution. That is, we introduce additional refining stages over the set of active patterns included in the model. These refining stages can be thought as a backfitting algorithm that tries to fix some of the mistakes that result from the greedy approach followed by the IVM. Experimental comparison of the proposed method with a full Bayes Machine and a Support Vector Machine seems to confirm that the method is competitive with these two techniques. Statistical tests are also carried out to support these results.
Year
DOI
Venue
2008
10.1007/978-3-540-87536-9_22
ICANN (1)
Keywords
Field
DocType
binary classification,additional refining stage,greedy approach,support vector machine,bayes machine,full bayes machine,sparse bayes machines,refining stage,active pattern,informative vector machine,statistical test,posterior distribution,sparse representation
Structured support vector machine,Binary classification,Pattern recognition,Computer science,Support vector machine,Sparse approximation,Artificial intelligence,Relevance vector machine,Backfitting algorithm,Machine learning,Statistical hypothesis testing,Bayes' theorem
Conference
Volume
ISSN
Citations 
5163
0302-9743
0
PageRank 
References 
Authors
0.34
9
1
Name
Order
Citations
PageRank
Daniel Hernández-Lobato144026.10