The nature of statistical learning theory
The nature of statistical learning theory
SIAM Review
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik-Chervonenkis Dimension
IEEE Transactions on Knowledge and Data Engineering
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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It is well-known that the major task of the SVM approach lies in the selection of its kernel. The quality of kernel will determine the quality of SVM classifier directly. However, the best choice of a kernel for a given problem is still an open research issue. This paper presents a novel method which learns SVM kernel by transforming it into a standard semi-definite programming (SDP) problem and then solves this SDP problem using various existing methods. Experimental results are presented to prove that SVM with the kernel learned by our proposed method outperforms that with a single common kernel in terms of generalization power.