Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
Several multiagent reinforcement learning (MARL) algorithms have been proposed to optimize agents' decisions. Only a subset of these MARL algorithms both do not require agent...
In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. ...
Prediction suffix trees (PST) provide a popular and effective tool for tasks such as compression, classification, and language modeling. In this paper we take a decision theoretic...
We review a multiple kernel learning (MKL) technique called p regularised multiple kernel Fisher discriminant analysis (MK-FDA), and investigate the effect of feature space denois...