STOCHASTIC SUBGRADIENT APPROACH FOR SOLVING
LINEAR SUPPORT VECTOR MACHINES – AN OVERVIEW
This paper is an overview of a recent approach for solving linear support vector machines (SVMs), the PEGASOS algorithm. The algorithm is based on a technique called the stochastic subgradient descent and employs it for solving the optimization problem posed by the soft margin SVM - a very popular classifier. We briefly introduce the SVM problem and one of the widely used solvers, SVM light, then describe the PEGASOS algorithm and present some experiments. We conclude that the algorithm efficiently discovers suboptimal solutions to large scale problems within a matter of seconds.