We present a new machine learning framework called "self-taught learning" for using unlabeled data in supervised classification tasks. We do not assume that the unlabele...
Rajat Raina, Alexis Battle, Honglak Lee, Benjamin ...
Spatial associative classification takes advantage of employing association rules for spatial classification purposes. In this work, we investigate spatial associative classificati...
This paper describes an investigation into the refinement of context-based human behavior models through the use of experiential learning. Specifically, a tactical agent was endow...
In this paper, we investigate the hypothesis that plan recognition can significantly improve the performance of a casebased reinforcement learner in an adversarial action selectio...
Abstract. This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a s...