A technique for defining the architecture and weights of a neural image classifier

Abstract

An approach to setting the architecture and the initial weights of an artificial neural network for solving classification problems is presented. A nonneural phase finds an approximate solution to the classification problems by constraining the shape of classification regions. After an appropriate mapping into a neural net, neural training is applied to refine the solution. Results on an image recognition application are presented.<>