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A novel support vector machine (SVM) algorithm for regression problems is proposed in this paper. Each pattern in the original training set is converted into a pair of patterns, which are labeled by 1 and −1, respectively. Therefore, the regression problem can be considered as a classification problem. By optimizing the obtained decision function,(More)
Support vector machine (SVM) has been widely used for its outstanding performance. But, it still has flaws. One of them is that SVM is unit sensitive. In this paper, we analyze how will the different units effect the SVM. Then, we propose a preprocess method not only to conquer this flaw, but also improve the generalization precision of SVM. The preprocess(More)
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