Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVM

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

Bibtex Metadata Paper


Shai Fine, Katya Scheinberg


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.