文章基本信息

标题：A Software Measurement Using Artificial Neural Network and Support Vector Machine
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作者：Bhavendra Kumar Sinha ; Amit Sinhal ; Bhupendra Verma 等
期刊名称：International Journal of Software Engineering & Applications (IJSEA)
印刷版ISSN：0976-2221
电子版ISSN：0975-9018
出版年度：2013
卷号：4
期号：4
页码：41
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Today, Software measurement are based on various techniques such that neural network, Geneticalgorithm, Fuzzy Logic etc. This study involves the efficiency of applying support vector machine usingGaussian Radial Basis kernel function to software measurement problem to increase the performance andaccuracy. Support vector machines (SVM) are innovative approach to constructing learning machines thatMinimize 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, objectdetection, face verification, text categorization, and so on. The result demonstrated that the accuracy andgeneralization performance of SVM Gaussian Radial Basis kernel function is better than RBFN. We alsoexamine and summarize the several superior points of the SVM compared with RBFN.
关键词：Support Vector Machine (SVM); Radial Basis Function (RBF) Network; Software Measurement.