Critical support vector machine without kernel function

Abstract

The drawback of SVM technique leads to a positive semidefinite quadratic programming problem with a dense, structured, positive semi-definite matrix, and also requires a set of kernel functions. We propose the learning algorithms that do not need any kernel functions. The separability is based on the critical Support vectors (CSV) essential to determine the locations of all separating hyperplanes. The algorithms give better performance compared with the other proposed SVM-based algorithms when they are tested with 2 spirals problem, Sonar, Ionosphere, Mushroom, Liver disorder, Cleveland heart, Pima diabetes, Tic Tac Toe, and Votes.