Modified Hebbian learning for large object classes using Neocognitron visual recognition

Thomas Y. P. Lee; Clark C. Guest

doi:10.1117/12.130822

16 December 1992 Modified Hebbian learning for large object classes using Neocognitron visual recognition

Thomas Y. P. Lee, Clark C. Guest

Proceedings Volume 1766, Neural and Stochastic Methods in Image and Signal Processing; (1992) https://doi.org/10.1117/12.130822
Event: San Diego '92, 1992, San Diego, CA, United States

Abstract

Hebbian learning law plays a very important role in the feedforward learning of neural networks. In multidimensional image space, particularly in vision, the asymmetric multidimensional Hebbian learning law can perform principal component feature extraction, thus providing high dimensional feature analysis and feature separation. In this paper, we verified this principle with modified Hebbian learning when applied to Fukushima's neocognitron visual recognition architecture.

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Thomas Y. P. Lee and Clark C. Guest "Modified Hebbian learning for large object classes using Neocognitron visual recognition", Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); https://doi.org/10.1117/12.130822

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