We discuss the integration of the expectation-maximization (EM) algorithm for maximum likelihood learning of Bayesian networks with belief propagation algorithms for approximate i...
An adaptive object recognition scheme for image sequences of many object scenes is described. The scheme is applied for t r d c object recognition under ego-motion. The recursive ...
We describe an extension to the Mixture of Experts architecture for modelling and controlling dynamical systems which exhibit multiple modesof behavior. This extension is based on...
This paper explores the effect of initial weight selection on feed-forward networks learning simple functions with the back-propagation technique. We first demonstrate, through th...
Case studies are widely used in business and medicine to help students learn from the successes and failures of practitioners in the field. This paper discusses the potential bene...
Kay A. Robbins, Catherine Sauls Key, Keith Dickins...