A Study on Enhanced Self-Generation Supervised Learning Algorithm for Image Recognition 


Vol. 30,  No. 2, pp. 31-40, Feb.  2005


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  Abstract

we propose an enhanced self-generation supervised algorithm that by combining an ART algorithm and the delta-bar-delta method. Form the input layer to the hidden layer, ART-1 and ART-2 are used to produce nodes, respectively. A winner-take-all method is adopted to the connection weight adaption so that a stored pattern for some pattern is updated.
we test the recognition of student identification, a certificate of residence, and an identifier from container that require nodes of hidden layers in neural network. In simulation results, the proposed self-generation supervised learning algorithm reduces the possibility of local minima and improves learning speed and paralysis than conventional neural networks.

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  Cite this article

[IEEE Style]

T. Kim, K. Kim, J. Paik, "A Study on Enhanced Self-Generation Supervised Learning Algorithm for Image Recognition," The Journal of Korean Institute of Communications and Information Sciences, vol. 30, no. 2, pp. 31-40, 2005. DOI: .

[ACM Style]

Tae-kyung Kim, Kwang-baek Kim, and Joon-ki Paik. 2005. A Study on Enhanced Self-Generation Supervised Learning Algorithm for Image Recognition. The Journal of Korean Institute of Communications and Information Sciences, 30, 2, (2005), 31-40. DOI: .

[KICS Style]

Tae-kyung Kim, Kwang-baek Kim, Joon-ki Paik, "A Study on Enhanced Self-Generation Supervised Learning Algorithm for Image Recognition," The Journal of Korean Institute of Communications and Information Sciences, vol. 30, no. 2, pp. 31-40, 2. 2005.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880