Support vector regression based on manifold regularization and its application
基于流形正则化的支持向量回归及应用

Based on the theory of manifold regularization, a new algorithm about semi-supervised learning for the problem of regression was proposed. The algorithm was deduced by the connection between the regularization term on the manifold and the classical regularization term. Using the result of support vector regression, the algorithm not only solves the problem about semi- supervised learning but also improves generalization capability. Numerical experimental results show that the algorithm enhances generalization capability and is strongly robust to noise, and has higher learning precision compared to support vector regression.