Browsing by Author "Piyabute Fuangkhon"
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    An Adaptive Learning Algorithm for Supervised Neural Network with Contour Preserving Classification
    ( 2009-11) Piyabute Fuangkhon ; Thitipong Tanprasert
    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 results convincingly confirm the effectiveness of the algorithm and the improvement of noise tolerance.
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    An incremental learning algorithm for supervised neural network with contour preserving Classification
    ( 2009-05) Piyabute Fuangkhon ; Thitipong Tanprasert
    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.
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    Multi-class contour preserving classification
    ( 2012-08) Piyabute Fuangkhon ; Thitipong Tanprasert
    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 datasets from UCI ma- chine learning repository present that the proposed technique supports multi-class data and can improve the level of accuracy of multi-class classification more effectively.
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    Multi-class contour preserving classification
    ( 2012-08) Piyabute Fuangkhon ; Thitipong Tanprasert
    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 datasets from UCI ma- chine learning repository present that the proposed technique supports multi-class data and can improve the level of accuracy of multi-class classification more effectively.
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    Reduced Multi-class Contour Preserving Classification
    ( 2016) Piyabute Fuangkhon ; Thitipong Tanprasert ; Assumption University. Vincent Mary School of Science and Technology
    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 only MCOVs located at the decision boundary between consecutive classes of data. The three MCOV reduction methods include the FF-AA reduction method, the FA-AF reduction method, and the FAF-AFA reduction method. An evaluation has been conducted to show the reduction capability, the contour preservation capability, and the levels of classification accuracy of the three MCOV reduction methods on both non-overlapping and highly overlapping synthetic-problem data sets and highly overlapping real-world data sets. For non-overlapping problems, the experimental results present that the FA-AF reduction method can partially reduce the number of MCOVs while preserving the contour of the problem most accurately and obtaining similar levels of classification accuracy as when the whole set of MCOVs is used. For highly overlapping problems, the experimental results present that the FF-AA reduction method can partially reduce the number of MCOVs while preserving the contour of the problem most accurately and obtaining similar levels of classification accuracy as when the whole set of MCOVs is used.