Now showing items 1-5 of 5

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    An adaptive learning algorithm for supervised neural network with contour preserving Classification 

    Piyabute Fuangkhon; Thitipong Tanprasert (2009-11)

    A study of noise tolerance characteristics of an adaptive learning algorithm for supervised neural network is presented in this paper. The algorithm allows the existing knowledge to age out in slow rate as a supervised neural network is gradually retrained with consecutive sets of new samples, resembling the change of application locality under a consistent environment. The algorithm utilizes the contour preserving classification algorithm to pre-process the training data to improve the classification and the noise tolerance. The experimental ...
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    An incremental learning algorithm for supervised neural network with contour preserving Classification 

    Piyabute Fuangkhon; Thitipong Tanprasert (2009-05)

    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 ...
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    Multi-class contour preserving classification 

    Piyabute Fuangkhon (Bangkok : Assumption University, 2013)
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    Multi-class Contour Preserving Classification 

    Piyabute Fuangkhon; Thitipong Tanprasert (2012-08)

    The original contour preserving classification technique was proposed to improve the robustness and weight fault tolerance of a neu- ral network applied with a two-class linearly separable problem. It was recently found to be improving the level of accuracy of two-class classi- fication. This paper presents an augmentation of the original technique to improve the level of accuracy of multi-class classification by better preservation of the shape or distribution model of a multi-class problem. The test results on six real world multi-class ...
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    Reduced Multi-class Contour Preserving Classification 

    Piyabute Fuangkhon (2016-06)

    This research presents the augmentation of the original contour preserving classification technique to support multi-class data and to reduce the number of synthesized vectors, called multi-class outpost vectors (MCOVs). The technique has been proven to function on both synthetic-problem data sets and real-world data sets correctly. The technique also includes three methods to reduce the number of MCOVs by using minimum vector distance selection between fundamental multi-class outpost vectors and additional multi-class outpost vectors to select ...