Abstract
This paper provides a comparative study on support vector regression (SVR), radial basis functions neural networks (RBFNs) and linear regression for estimation of software project effort. We have considered SVR with linear as well as RBF kernels. The experiments were carried out using a dataset of software projects from NASA and the results have shown that SVR significantly outperforms RBFNs and linear regression in this task.
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