GENETIC SELECTION AND NEURAL MODELING OF PIECEWISE-LINEAR CLASSIFIERS
Jack Sklansky, Mark R. Vriesenga · International Journal of Pattern Recognition and Artificial Intelligence · 1996
Piecewise-linear mathematical structures form a convenient and important framework for implementing trainable and adaptive pattern classifiers. Neural networks and genetic algorithms offer additional approaches with important benefits for the design of such classifiers. In this paper we show how neural modeling and genetic selection can be applied to piecewise-linear structures to optimize both the topology and the parameter values of the network forming the classifier. Such a classifier will tend to have a low error rate and high robustness. We describe applications of these techniques to an adaptive detector of abnormal tissue in mammograms and a detector of straight lines and edges in noisy aerial images.