Linear separability: Quasisecant method and application to semi-supervised data classification

Abstract

In this paper we have proposed a semi-supervised algorithm based on quasisecant optimization method for solving data classification problems. The algorithm computes hyperplane(s) to separate two sets with respect to some tolerance. An error function is formulated and an algorithm for its minimization is expressed. We present results of numerical experiments using several UCI test data sets and compare the proposed algorithm with two supervised data classification algorithm (linear separability, max-min separability) and two support vector machine solvers (LIBSVM and SVM-light). © Izmir University of Economics, Turkey, 2010.