An Effective Regularization Path for ν-Support Vector Classification

The ν-Support Vector Classification (ν-SVC) proposed by Scholkopf et al. has the advantage of using a regularization parameter ν on controlling the number of support vectors and margin errors. However, comparing to C-SVC, its formulation is more complicated, up to now there are no effective methods on computing the regularization path for it. In this paper, we propose a new regularization path algorithm, which is designed based on a modified formulation of ν-SVC and traces the solution path with respect to the parameter ν.