Pittipol Kantavat

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Support vector machine (SVM) is a very powerful machine learning algorithm that can be applied to many kinds of applications, not only computation sciences but investing tasks also. This paper presents a new algorithm combining SVM with technical analysis for investing in stocks. RReliefF feature selection is used to choose the appropriate training and(More)
We propose new methods for Support Vector Machines (SVMs) using tree architecture for multi-class classification. In each node of the tree, we select an appropriate binary classifier using entropy and generalization error estimation, then group the examples into positive and negative classes based on the selected classifier and train a new classifier for(More)
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