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Proceedings Paper

Modified Hebbian learning for large object classes using Neocognitron visual recognition
Author(s): Thomas Y. P. Lee; Clark C. Guest
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Paper 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.

Paper Details

Date Published: 16 December 1992
PDF: 12 pages
Proc. SPIE 1766, Neural and Stochastic Methods in Image and Signal Processing, (16 December 1992); doi: 10.1117/12.130822
Show Author Affiliations
Thomas Y. P. Lee, Univ. of California/San Diego (United States)
Clark C. Guest, Univ. of California/San Diego (United States)

Published in SPIE Proceedings Vol. 1766:
Neural and Stochastic Methods in Image and Signal Processing
Su-Shing Chen, Editor(s)

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