Applications of learning strategies to pattern recognition

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Abstract
Experiments are described with a hybrid learning system that automates the generation of multiclass pattern recognition systems. The learning system utilizes genetic algorithms to formulate a small set of feature detectors and an adaptive neural network to classify feature vectors. The experiments utilize a training set of handprinted characters and a pool of randomly generated morphological hit-or-miss detectors. A major problem is selecting a small subset of cooperating detectors from a large, easily generated pool of detectors. A new method of selecting detectors from a pool is presented that utilizes a modified version of crossover operators from genetic algorithms. This new crossover approach for generating feature detectors is evaluated by comparing it with random selection and with a restricted random mutation approach. The system adaptively adjusts the size of detector sets and the corresponding number of neural net nodes.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

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