—Biasing properly the hypothesis space of a learner has been shown to improve generalization performance. Methods for achieving this goal have been proposed, that range from desi...
We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual...
: Most of Knowledge Discovery in Database (KDD) systems are integrating efficient Machine Learning techniques. In fact issues in Machine Learning and KDD are very close allowing fo...
Jean-Daniel Zucker, Vincent Corruble, J. Thomas, G...
A major problem in machine learning is that of inductive bias: how to choose a learner's hypothesis space so that it is large enough to contain a solution to the problem bein...
Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize th...