Fast Stochastic Frank-Wolfe Algorithms for Nonlinear SVMs

The high computational cost of nonlinear support vector machines has limited their usability for large-scale problems. We propose two novel stochastic algorithms to tackle this problem. These algorithms are based on a simple and classic optimization method: the Frank-Wolfe method, which is known to be fast for problems with a large number of linear constraints. Formulating the nonlinear SVM problem to take advantage of this method, we achieve a provable time complexity of O(dQ/2). The proposed algorithms achieve comparable or even better accuracies than the state-of-theart methods, and are significantly faster.