Support vector machine with the fuzzy hybrid kernel for protein subcellular localization classification

被引：0

作者：

Jin, B ^{[1
]}

Tang, YC ^{[1
]}

Zhang, YQ ^{[1
]}

Lu, CD ^{[1
]}

Weber, I ^{[1
]}

机构：

[1] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30302 USA

来源：

FUZZ-IEEE 2005: Proceedings of the IEEE International Conference on Fuzzy Systems: BIGGEST LITTLE CONFERENCE IN THE WORLD | 2005年

关键词：

fuzzy hybrid kernels; TSK model; support vector machine; kernel machines; bioinformatics;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In the paper we present a fuzzy hybrid kernel that combines several conventional kernels by using the TSK model. The major technical merit is to make a more reliable kernel fusing different kernels. Support Vector Machine (SVM) with the fuzzy hybrid kernel is employed for protein subcellular localization classification. Experimental results indicate that SVM with the new fuzzy hybrid kernel is better than those with conventional kernels.

引用

页码：420 / 423

页数：4

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