Abstract
The support vector machine (SVM) learning algorithm is a method for small samples learning, but the selected support vectors (SVs) must be obtained by an optimal algorithm. To counter the low speed of the SVM learning, a new fast method combining SVM and a fuzzy method is proposed. The SVs are pre-extracted by an iterative algorithm and a fuzzy method is used instead of solving the complex quadratic program problem. The method greatly reduces the training samples and improves the speed of SVM learning, while the ability of the SVM is not degraded. Better results are obtained over other SVM methods, which makes this new fuzzy pre-extracting SVM method useful in practice.
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