Statistical pattern recognition with neural networks: benchmarking studies

Abstract

Three basic types of neural-like networks (backpropagation network, Boltzmann machine, and learning vector quantization), were applied to two representative artificial statistical pattern recognition tasks, each with varying dimensionality. The performance of each network's approach to solving the tasks was evaluated and compared, both to the performance of the other two networks and to the theoretical limit. The learning vector quantization was further benchmarked against the parametric Bayes classifier and the k-nearest-neighbor classifier using natural speech data. A novel learning vector quantization classifier called LVQ2 is introduced.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>