Abstract
A competitive learning neural network (CLNN) has a mechanism to discover statistically distinctive features included in input population. Competitive learning is different from a classification paradigm that needs a supervisor. Therefore, the unknown features are expected to be extracted from the visual image. However, CLNN has a problem of a serious decline of learning ability from the lack of competition. The reason for this is that the units of CLNN are not allocated to adapt to the distribution of input vectors in the feature space. We propose learning algorithms to optimize the positions of units and attain valid competition. These learning algorithms are based on structure learning according to two ideas. The first idea is that many units should be allocated according to concentrations of input vectors in the feature space. The second idea is that at least one unit should exist within an appropriate distance form every input vector. We apply the proposed algorithms to CLNN and experiment on the distinction of different binary 64 X 64 dot patterns. This patterns explores the validity of the two algorithms for CLNN.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.
Published Version
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