Support Vector Regression for Prediction of Housing Values

Support vector regression is based on statistical learning theory under the framework of a new general-purpose machine learning method, which is a effective way to deal with nonlinear classification and nonlinear regression. Due to the comprehensive theoretical basis and excellent learning performance, The technology has become the current international machine learning research community hot spots, which can to better address the practical problem, such as the small sample and high dimension, nonlinear and local minima etc.. In the article, support vector regression (SVR) and the RBF neural network do function fitting tests, using simulation data, and the results are compared and evaluation. And use the SVR algorithm to solve practical problems in the area of real estate for predict housing values, with a view to consumers in the choice of housing to provide good guidance.