An incremental learning algorithm for supervised neural network with contour preserving Classification
An incremental learning algorithm for supervised neural network with contour preserving Classification
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2009-05
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eng
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application/pdf
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4 pages
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Proceedings of the 6th International Conference on Electrical Engineering/Electronics Computer, Telecommunication and Information Technology (ECTI-CON 2009). Pattaya, Chonburi, Thailand, (May 6-9, 2009), 740-743
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Abstract
This paper presents an alternative algorithm for integrating the existing knowledge of a supervised learning neural network with the new training data. The algorithm allows the existing knowledge to age out in slow rate as a neural network is gradually retrained with consecutive sets of new samples, resembling the change of application locality under a consistent environment. The algorithm also utilizes the contour preserving classification algorithm to increase the accuracy of classification. The experiment is performed on 2-dimension partition problem and the result convincingly confirms the effectiveness of the algorithm.