Angle-based twin parametric-margin support vector machine for pattern classification

Abstract

In this paper, a novel angle-based twin parametric-margin support vector machine (ATP-SVM) is proposed, which can efficiently handle heteroscedastic noise. Taking motivation from twin parametric-margin support vector machine (TPMSVM), ATP-SVM determines two nonparallel parametric-margin hyperplanes, such that the angle between their normal is maximized. Unlike TPMSVM, it solves only one modified quadratic programming problem (QPP) with fewer number of representative samples. Further, it avoids the explicit computation of inverse of matrices in the dual and has efficient learning time as compared to other single problem classifiers like nonparallel SVM based on one optimization problem (NSVMOOP).The efficacy of ATP-SVM is tested by conducting experiments on a wide range of benchmark UCI datasets. ATP-SVM is extended for multi-category classification using state-of-the-art one-against-all (OAA) and binary tree (BT) based multi-category classification approaches. This work also proposes the application of ATP-SVM for segmentation of color images.