Statistical pattern recognition with neural networks: benchmarking studies.

Abstract

Successful recognition of natural signals, e.g., speech recognition, requires substantial statistical pattern recognition capabilities. This is at odds with the fact that the bulk of work on applying neural networks to pattern recognition has concentrated on non-statistical problems. Three basic types of neural-like networks (Backpropagation network, Boltzmann machine, and Learning Vector Qumtization), were applied in this work to two representative artificial statistical pattern recognition tasks, each with varying dimensionality. The performance of each network's different 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-nearestneighbor classifier using natural speech data. A novel Learning Vector Quantization classifier (LVQ2) is introduced the first time in this work.