This paper analyses the Contrastive Divergence algorithm for learning statistical parameters. We relate the algorithm to the stochastic approximation literature. This enables us t...
Refinement operators for theories avoid the problems related to the myopia of many relational learning algorithms based on the operators that refine single clauses. However, the n...
Nicola Fanizzi, Stefano Ferilli, Nicola Di Mauro, ...
: This paper presents preliminary findings of an research project in which the research partners, academics and a telecommunication labour union, are attempting to understand, lear...
We show that, given data from a mixture of k well-separated spherical Gaussians in Rd, a simple two-round variant of EM will, with high probability, learn the parameters of the Ga...
Many events in news articles don't include time arguments. This paper describes two methods, one based on rules and the other based on statistical learning, to predict the un...