Probability Fuzzy Support Vector Machines
نویسندگان
چکیده
In this paper, a model of probability fuzzy support vector machines (PFSVMs) based on the consideration both for fuzzy clustering and probability distributions is proposed. In many applications of traditional support vector machines (SVMs), there are over-fitting problems due to the fact that SVM is sensitive to outliers or noises. In order to solve the problem, the fuzzy support vector machines (FSVMs) model is established. However, in the case that two points are with the same membership, the more information of their influence cannot be carried out by FSVM. The proposed model is based on the consideration that there is not only existing classification distribution but also probability distribution among samples. Experiments show that compared with SVM and FSVM, PFSVM has a better prediction and the classification performance.
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