Lin Chun-Fu, Wang Sheng-De
Dept. of Electr. Eng., Nat. Taiwan Univ.
IEEE Trans Neural Netw. 2002;13(2):464-71. doi: 10.1109/72.991432.
A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions to the learning of decision surface. We call the proposed method fuzzy SVMs (FSVMs).
支持向量机（SVM）从两类不同的输入点中学习决策面。在许多应用中，每个输入点可能无法完全归属于这两类中的某一类。在本文中，我们对每个输入点应用模糊隶属度，并重新构建支持向量机，使得不同的输入点对决策面的学习能做出不同的贡献。我们将所提出的方法称为模糊支持向量机（FSVM）。
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