A Software Measurement Using Artificial Neural Network and Support Vector Machine

Abstract

Today, software measurement are based on various techniques such that neural network, Genetic algorithm, Fuzzy Logic etc. This study involves the efficiency of applying support vector machine using Gaussian Radial Basis kernel function to software measurement problem to increase the performance and accuracy. Support vector machines (SVM) are innovative approach to constructing learning machines that Minimize generalization error. There is a close relationship between SVMs and the Radial Basis Function (RBF) classifiers. Both have found numerous applications such as in optical character recognition, object detection, face verification, text categorization, and so on. The result demonstrated that the accuracy and generalization performance of SVM Gaussian Radial Basis kernel function is better than RBFN. We also examine and summarize the several superior points of the SVM compared with RBFN.