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G. R.G. Lanckrient, N. Cris-tianini, P. Bartlett, L. El Ghaoui, M.I. Jordan. Learning the kernel matrix with semidefinite programming. Jour-nal of Machine Learning Research, 5, 2004, pp. 27-72.

has been cited by the following article:

• TITLE: Text Classification Using Support Vector Machine with Mixture of Kernel

  AUTHORS: Liwei Wei, Bo Wei, Bin Wang

  KEYWORDS: Text Classification; SVM-MK; Feature selection; Classification model; SVM

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.12B, January 18, 2013

  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.