Differential theory of learning for efficient neural network pattern recognition

John B. Hampshire II; Bhagavatula Vijaya Kumar

doi:10.1117/12.152523

2 September 1993 Differential theory of learning for efficient neural network pattern recognition

John B. Hampshire II, Bhagavatula Vijaya Kumar

Proceedings Volume 1965, Applications of Artificial Neural Networks IV; (1993) https://doi.org/10.1117/12.152523
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

Abstract

We describe a new theory of differential learning by which a broad family of pattern classifiers (including many well-known neural network paradigms) can learn stochastic concepts efficiently. We describe the relationship between a classifier's ability to generate well to unseen test examples and the efficiency of the strategy by which it learns. We list a series of proofs that differential learning is efficient in its information and computational resource requirements, whereas traditional probabilistic learning strategies are not. The proofs are illustrated by a simple example that lends itself to closed-form analysis. We conclude with an optical character recognition task for which three different types of differentially generated classifiers generalize significantly better than their probabilistically generated counterparts.

Citation Download Citation

John B. Hampshire II and Bhagavatula Vijaya Kumar "Differential theory of learning for efficient neural network pattern recognition", Proc. SPIE 1965, Applications of Artificial Neural Networks IV, (2 September 1993); https://doi.org/10.1117/12.152523

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