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 ...
Discovering rare categories and classifying new instances of them is
an important data mining issue in many fields, but fully supervised
learning of a rare class classifier is pr...
This paper examines the problem of finding an optimal policy for a Partially Observable Markov Decision Process (POMDP) when the model is not known or is only poorly specified. W...
Abstract. Query-by-example and query-by-keyword both suffer from the problem of "aliasing," meaning that example-images and keywords potentially have variable interpretat...
Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and h...