TY - GEN
T1 - A Bayesian active learning framework for a two-class classification problem
AU - Ruiz, Pablo
AU - Mateos, Javier
AU - Molina, Rafael
AU - Katsaggelos, Aggelos K.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84867128815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84867128815&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-32436-9_4
DO - 10.1007/978-3-642-32436-9_4
M3 - Conference contribution
AN - SCOPUS:84867128815
SN - 9783642324352
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 42
EP - 53
BT - Computational Intelligence for Multimedia Understanding - International Workshop, MUSCLE 2011, Revised Selected Papers
T2 - International Workshop on Multimedia Understanding Through Semantics, Computation, and Learning, MUSCLE 2011
Y2 - 13 December 2011 through 15 December 2011
ER -