Self organizing neural networks with a split/merge algorithm

Authors:
A. D. Kulkarni;G. M. Whitson
Affiliations:
Computer Science Department, The University of Texas at Tyler, Tyler, TX;Computer Science Department, The University of Texas at Tyler, Tyler, TX
Venue:
SIGSMALL '90 Proceedings of the 1990 ACM SIGSMALL/PC symposium on Small systems
Year:
1990

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Abstract

In this paper we present a new learning algorithm for Artificial Neural Networks (ANN) using a split/merge technique. An ANN model with the new algorithm has been developed and tested on a PC. The model detects the similarity between the input patterns, and identifies the number of categories present in the input samples. The algorithm is similar to a competitive learning algorithm. Unlike the competitive learning algorithm, in this algorithm we use two types of weights: long term weights (LTWs) and short term weights (STWs). The network stability is provided by the LTWs, whereas the network plasticity is provided by STWs. As an illustration, the model is used to categorize pixels in a multispectral image. The categorization is based on the observed spectral signature at each pixel.