Fuzzy kernel perceptron

被引：54

作者：

Chen, JH ^{[1
]}

Chen, CS ^{[1
]}

机构：

[1] Acad Sinica, Inst Sci Informat, Taipei, Taiwan

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2002年 / 13卷 / 06期

关键词：

classification; fuzzy perceptron (FP); kernel-based method; Mercer kernel; supervised learning; support vector machine (SVM);

D O I：

10.1109/TNN.2002.804311

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A new learning method, the fuzzy kernel perceptron (FKP), in which the fuzzy perceptron (FP) and the Mercer kernels are incorporated, is proposed in this paper. The proposed method first maps the input data into a high-dimensional feature space using some implicit mapping functions. Then, the FP? is adopted to find a linear separating hyperplane in the high-dimensional feature space. Compared with the FP, the FKP is more suitable for solving the linearly nonseparable problems. In addition, it is also more efficient than the kernel perceptron (KP). Experimental results show that the FKP has better classification performance than FP, KP, and the support vector machine (SVM).

引用

页码：1364 / 1373

页数：10

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