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Text Classification Using Support Vector Machine with Mixture of Kernel

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DOI: 10.4236/jsea.2012.512B012    4,558 Downloads   6,442 Views   Citations

ABSTRACT

Recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for text classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a text classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. A real Chinese corpus from FudanUniversityis used to demonstrate the good performance of the SVM- MK.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

L. Wei, B. Wei and B. Wang, "Text Classification Using Support Vector Machine with Mixture of Kernel," Journal of Software Engineering and Applications, Vol. 5 No. 12B, 2012, pp. 55-58. doi: 10.4236/jsea.2012.512B012.

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