NEURAL NETWORKS APPLIED TO VISUAL PATTERN RECOGNITION - A COMPARATIVE STUDY

In this paper, we discuss the principles of using back-propagation neural networks for pattern recognition. The basic information on neural networks learning is presented. A comparison is drawn between classification performance of the back-propagation network and other pattern recognition methods. Special attention is paid to preprocessing of the required data. The following problems are discussed in detail: choosing network topology, selecting proper values for learning coeficients, and estimating the quality of learning and recognition processes.