Piyabute Fuangkhon

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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(More)
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)
This paper presents an alternative algorithm that can reduce the size of a training set for a feed-forward neural network while obtaining similar levels of accuracy of classification as when the whole training set is used. The algorithm processes the training set by selecting only input vectors at the boundary between consecutive classes of data to be(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)