**LIBSVM FAQ**

No, libsvm solves linear/nonlinear SVMs by the same way. Some tricks may save training/testing time if the linear kernel is used, so libsvm is NOT particularly efficient for linear SVM, especially when C is large and the number of data is much larger than the number of attributes. You can either

Use small C only. We have shown in the following paper that after C is larger than a certain threshold, the decision function is the same.

S. S. Keerthi and C.-J. Lin. Asymptotic behaviors of support vector machines with Gaussian kernel . Neural Computation, 15(2003), 1667-1689.

Check liblinear, which is designed for large-scale linear classification.

Please also see our SVM guide on the discussion of using RBF and linear kernels."

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