A genetic sparse distributed memory approach to the application of handwritten character recognition

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Title:	A genetic sparse distributed memory approach to the application of handwritten character recognition
Authors:	Fan,KC;Wang,YK
Contributors:	資訊工程研究所
Date:	1997
Issue Date:	2010-06-29 20:15:57 (UTC+8)
Publisher:	中央大學
Abstract:	Kanerva's Sparse Distributed Memory (SDM) is one of the self-organizing neural networks that mimic closely the psychological behavior of the human brain. In this paper, a Genetic Sparse Distributed Memory (GSDM) model that combines SDM with genetic algorithms is proposed. The proposed GSDM model not only maintains the advantages of both SDM and genetic algorithms, but also has higher memory utilization to improve the recognition rate. Its effective performance is also verified by application to Optical Character Recognition (OCR). Experimental results reveal the feasibility and validity of the proposed model. (C) 1997 Pattern Recognition Society. Published by Elsevier Science Ltd.
Relation:	PATTERN RECOGNITION
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] journal & Dissertation

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