A Simple Method for Solving the SVM Regularization Path for Semidefinite Kernels.

The support vector machine (SVM) remains a popular classifier for its excellent generalization performance and applicability of kernel methods; however, it still requires tuning of a regularization parameter, C , to achieve optimal performance. Regularization path-following algorithms efficiently solve the solution at all possible values of the regularization parameter relying on the fact that the SVM solution is piece-wise linear in C . The SVMPath originally introduced by Hastie et al.

Efficient revised simplex method for SVM training.

Existing active set methods reported in the literature for support vector machine (SVM) training must contend with singularities when solving for the search direction. When a singularity is encountered, an infinite descent direction can be carefully chosen that avoids cycling and allows the algorithm to converge. However, the algorithm implementation is likely to be more complex and less computationally efficient than would otherwise be required for an algorithm that does not have to contend with the singularities.