The goal of active learning is to determine the locations of training input points so that the generalization error is minimized. We discuss the problem of active learning in line...
Due to the unavoidable fact that a robot’s sensors will be limited in some manner, it is entirely possible that it can find itself unable to distinguish between differing state...
In this paper, we describe a retrieval system that uses hidden annotation to improve the performance. The contribution of this paper is a novel active learning framework that can ...
Active learning is a proven method for reducing the cost of creating the training sets that are necessary for statistical NLP. However, there has been little work on stopping crit...
Several recent techniques for solving Markov decision processes use dynamic Bayesian networks to compactly represent tasks. The dynamic Bayesian network representation may not be g...