Cutting Plane Training for Linear Support Vector Machines

Abstract

Support Vector Machines (SVMs) have been shown to achieve high performance on classification tasks across many domains, and a great deal of work has been dedicated to developing computationally efficient training algorithms for linear SVMs. One approach [1] approximately minimizes risk through use of cutting planes, and is improved by [2], [3]. We build upon this work, presenting a modification to the algorithm developed by Franc and Sonnenburg [2]. We demonstrate empirically that our changes can reduce cutting plane training time by up to 40 percent, and discuss how changes in data sets and parameter settings affect the effectiveness of our method.

Two New Decomposition Algorithms for Training Bound-Constrained Support Vector Machines*

“…Several researchers also explore how to train the primal form of (4) and the extended models fast. The existing algorithms can be broadly categorized into two categories: the cutting-plane methods [11,5,12,13,25], and subgradient methods [3,17]. For example, in [17], Shalev-Shwartz et al described and analyzed a simple and effective stochastic sub-gradient descent algorithm and prove that the number of iterations required to obtain a solution of accuracy is O(1/ ).…”