Pattern Recognition via Support Vector Machine with Computationally Efficient Nonlinear Transform


We introduce a new “sparse” nonlinear transformation of the SVM feature space, which permits the use of efficient optimization techniques for finding separating hyperplane. Corresponding quadratic program can be solved in the primal formulation, so that complexity of solution grows only linearly with the number of training examples. The Penalty/Barrier… (More)