支持向量机及其在函数逼近中的应用

支持向量机是一种新的机器学习算法，它的理论基础是Vapnik创建的统计学习理论，它采用结构风险最小化准则，在最小化样本点误差的同时，缩小模型预测误差的上界，从而提高了模型的泛化能力，本文通过SVM在函数逼近中的应用，研究了SVM的小样本学习，泛化能力和抗噪声扰动能力。

Support Vector Machine and Its Applications to Function Approximation

Support vector machine is a new machine learning algorith m, based theoretically on statistic learning theory created by Vapnik. Employing the criteria of structural risk minimization, which minimizes the errors betwee n sample data and model data and decreases simultaneously the upper bound of p redict error of model, SVM's generalization is better than others. The character istics of SVM, such as the strong learning capability based on small samples, th e good characteristic of generalization and insensitivity to random noise distur bance, are shown by its applications to function approximation.