Piyabute Fuangkhon

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Outpost Vector model synthesizes new vectors from two classes of data at their boundary to maintain the shape of the current system in order to increase the level of accuracy of classification. This paper presents an incremental learning preprocessor for Feed-forward Neural Network (FFNN) which utilizes Outpost Vector model to improve the level of accuracy(More)
A framework presenting a basic conceptual structure used to solve adaptive learning problems in soft real time applications is proposed. Its design consists of two supervised neural networks running simultaneously. One is used for training data and the other is used for testing data. The accuracy of the classification is improved from the previous works by(More)
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(More)
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