Incremental Learning and Selective Sampling via Parametric Optimization Framework for SVM
Shai Fine, Katya Scheinberg · The MIT Press eBooks · 2002
We propose a framework based on a parametric quadratic programming (QP) technique to solve the support vector machine (SVM) training problem. This framework, can be specialized to obtain two SVM optimization methods. The rst solves the xed bias problem, while the second starts with an optimal solution for a xed bias problem and adjusts the bias until the optimal value is found.