A Software Measurement Using Artificial Neural Network and Support Vector Machine
Bhavendra Kumar Sinha1 , Amit Sinhal2 and Bhupendra Verma3
1M.Tech. (IT) Student, Department of Information Technology, TIT, Bhopal
2Professor, Department of Computer Science & Engineering, TIT, Bhopal
3 Director, TIT (Excellence), Bhopal
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
KEYWORDS
Support Vector Machine (SVM), Radial Basis Function (RBF) Network, Software Measurement.
For More Details : http://airccse.org/journal/ijsea/papers/4413ijsea04.pdf
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