A Bayesian active learning framework for a two-class classification problem

Pablo Ruiz*, Javier Mateos, Rafael Molina, Aggelos K. Katsaggelos

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper we present an active learning procedure for the two-class supervised classification problem. The utilized methodology exploits the Bayesian modeling and inference paradigm to tackle the problem of kernel-based data classification. This Bayesian methodology is appropriate for both finite and infinite dimensional feature spaces. Parameters are estimated, using the kernel trick, following the evidence Bayesian approach from the marginal distribution of the observations. The proposed active learning procedure uses a criterion based on the entropy of the posterior distribution of the adaptive parameters to select the sample to be included in the training set. A synthetic dataset as well as a real remote sensing classification problem are used to validate the followed approach.

Original languageEnglish (US)
Title of host publicationComputational Intelligence for Multimedia Understanding - International Workshop, MUSCLE 2011, Revised Selected Papers
Pages42-53
Number of pages12
DOIs
StatePublished - Oct 10 2012
EventInternational Workshop on Multimedia Understanding Through Semantics, Computation, and Learning, MUSCLE 2011 - Pisa, Italy
Duration: Dec 13 2011Dec 15 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7252 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshop on Multimedia Understanding Through Semantics, Computation, and Learning, MUSCLE 2011
CountryItaly
CityPisa
Period12/13/1112/15/11

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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