We propose a new approach for learning Bayesian classifiers from data. Although it relies on traditional Bayesian network (BN) learning algorithms, the effectiveness of our approa...
One promise that has always been made in the field of e-learning is the possibility to create and deliver learning material that is adaptable to individual learners. Realising thi...
In this paper, word sense dismnbiguation (WSD) accuracy achievable by a probabilistic classifier, using very milfimal training sets, is investigated. \Ve made the assuml)tiou that...
One of the most promising opportunities introduced by rapid advances in knowledge-based learning environments and multimedia technologies is the possibility of creating animated p...
In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning communit...