This paper re-examines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in on-line learning [1...
We consider the problem of learning mixtures of product distributions over discrete domains in the distribution learning framework introduced by Kearns et al. [18]. We give a poly...
Water systems often allow efficient water uses via water reuse and/or recirculation. Defining the network layout connecting water-using processes is a complex problem which involv...
In this paper we focus on the adaptation of boosting to grammatical inference. We aim at improving the performances of state merging algorithms in the presence of noisy data by us...
Jean-Christophe Janodet, Richard Nock, Marc Sebban...
In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intr...