UNSUPERVISED LEARNING PROCEDURES FOR NEURAL NETWORKS

Abstract

Supervised learning procedures for neural networks have recently met with considerable success in learning difficult mappings. However, their range of applicability is limited by their poor scaling behavior, lack of biological plausibility, and restriction to problems for which an external teacher is available. A promising alternative is to develop unsupervised learning algorithms which can adaptively learn to encode the statistical regularities of the input patterns, without being told explicitly the correct response for each pattern. In this paper, we describe the major approaches that have been taken to model unsupervised learning, and give an in-depth review of several examples of each approach.