We address the problem of efficiently learning Naive Bayes classifiers under classconditional classification noise (CCCN). Naive Bayes classifiers rely on the hypothesis that the ...
Gaussian Process Temporal Difference (GPTD) learning offers a Bayesian solution to the policy evaluation problem of reinforcement learning. In this paper we extend the GPTD framew...
In this paper, we discuss a technique for handling multi-class problems with binary classifiers, namely to learn one classifier for each pair of classes. Although this idea is kno...
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
We consider the problem of unsupervised classification of temporal sequences of facial expressions in video. This problem arises in the design of an adaptive visual agent, which m...