Learning Optimally Sparse Support Vector Machines

Andrew Cotter, Shai Shalev-Shwartz, Nati Srebro

Abstract: We show how to train SVMs with an optimal guarantee on the number of support vectors (up to constants), and with sample complexity and training runtime bounds matching the best known for kernel SVM...