SVM kernel functions for classification

Abstract

A new generation learning system based on recent advances in statistical learning theory deliver state-of-the-art performance in real-world applications that is Support Vector Machines [2]. Applications such as text categorization, hand-written character recognition, image classification, bio-sequence analysis [9] etc for the classification and regression Most of the existing supervised classification methods are based on traditional statistics, which can provide ideal results when sample size is tending to infinity. However, only finite samples can be acquired in practice. In this paper, a novel learning method, Support Vector Machine (SVM), is applied on different data. This paper emphasizes the classification task with Support Vector Machine with different kernel function. It has several kernel functions including linear, polynomial and radial basis for performing classification [13].