A genetic sparse distributed memory approach to the application of handwritten character recognition
Identifieur interne : 002453 ( Main/Exploration ); précédent : 002452; suivant : 002454A genetic sparse distributed memory approach to the application of handwritten character recognition
Auteurs : Kuo-Chin Fan [Taïwan, République populaire de Chine] ; Yuan-Kai Wang [République populaire de Chine]Source :
- Pattern Recognition [ 0031-3203 ] ; 1996.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
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.
Url:
DOI: 10.1016/S0031-3203(97)00017-4
Affiliations:
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Le document en format XML
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<front><div type="abstract" xml:lang="en">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.</div>
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