In this paper, it will be shown that it is feasible to extract finite state machines in a domain of, for rule extraction, previously unencountered complexity. The algorithm used i...
This paper describes an attempt to devise a knowledge discovery model that is inspired from the two theoretical frameworks of selectionism and constructivism in human cognitive le...
We present a model of creating a hierarchical set of rules that encode generalizations and exceptions derived from induction learning. The rules use the input features directly an...
Neural networks and the Kriging method are compared for constructing £tness approximation models in evolutionary optimization algorithms. The two models are applied in an identica...
This paper proposes three novel training methods, two of them based on the back-propagation approach and a third one based on information theory for Multilayer Perceptron (MLP) bin...