On Performing Classification Using SVM with Radial Basis and Polynomial Kernel Functions

Abstract

Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory deliver state-of-the-art performance in real-world applications such as text categorization, hand-written character recognition, image classification, bio-sequence analysis etc for the classification and regression. This paper emphasizes the classification task with Support Vector Machine. It has several kernel functions including linear, polynomial and radial basis for performing classification. Our comparison between polynomial and radial basis kernel functions for selected feature conclude that radial basis function is preferable than polynomial for large datasets.