Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVM

Abstract

We propose a framework based on a parametric quadratic program(cid:173) ming (QP) technique to solve the support vector machine (SVM) training problem. This framework, can be specialized to obtain two SVM optimization methods. The first solves the fixed bias prob(cid:173) lem, while the second starts with an optimal solution for a fixed bias problem and adjusts the bias until the optimal value is found. The later method can be applied in conjunction with any other ex(cid:173) isting technique which obtains a fixed bias solution. Moreover, the second method can also be used independently to solve the com(cid:173) plete SVM training problem. A combination of these two methods is more flexible than each individual method and, among other things, produces an incremental algorithm which exactly solve the 1-Norm Soft Margin SVM optimization problem. Applying Selec(cid:173) tive Sampling techniques may further boost convergence.