Exact SVM training by Wolfe's minimum norm point algorithm
Masashi Kitamura, Akiko Takeda, Satoru Iwata · 2014
This paper applies Wolfe's algorithm for finding the minimum norm point in a polytope to training of standard SVM with hinge loss. The resulting algorithm is guaranteed to obtain an exact optimal solution within a finite number of iterations. Experiments illustrate that our algorithm runs faster than existing algorithms such as LIBSVM for the same model. In comparison with LIBLINEAR, which adopts a variant of SVMs, our approach works better when the feature size is modest; the feature size is sufficiently smaller than the sample size.