We focus on the problem of efficient learning of dependency trees. Once grown, they can be used as a special case of a Bayesian network, for PDF approximation, and for many other u...
One approach in pursuit of general intelligent agents has been to concentrate on the underlying cognitive architecture, of which Soar is a prime example. In the past, Soar has reli...
Abstract. We present a new method for voting exponential (in the number of attributes) size sets of Bayesian classifiers in polynomial time with polynomial memory requirements. Tra...
In this paper we present a system that uses its underlying physiology, a hierarchical memory and a collection of memory management algorithms to learn concepts as cases and to bui...
Despite the well-known performances and the theoretical power of neural networks, learning and generalizing are sometimes very difficult. In this article, we investigate how short ...