Variational Bayes for Continuous Hidden Markov Models and Its Application to Active Learning

In this paper we present a variational Bayes (VB) framework for learning continuous hidden Markov models (CHMMs), and we examine the VB framework within active learning. Unlike a maximum likelihood or maximum a posteriori training procedure, which yield a point estimate of the CHMM parameters, VB-based training yields an estimate of the full posterior of the model parameters. This is particularly important for small training sets, since it gives a measure of confidence in the accuracy of the learned model. This is utilized within the context of active learning, for which we acquire labels for those feature vectors for which knowledge of the associated label would be most informative for reducing model-parameter uncertainty. Three active learning algorithms are considered in this paper: (i) query by committee (QBC), with the goal of selecting data for labeling that minimize the classification variance; (ii) a maximum expected information gain method that seeks to label data with the go...

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Shihao Ji, Balaji Krishnapuram, Lawrence Carin
• Keywords: Active Learning, Model Parameters, PAMI 2006, Posteriori Training Procedure