摘要
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 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.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2002年第5期555-559,568,共6页
Journal of East China University of Science and Technology
基金
宁波市科技攻关项目 (0 0 12 0 0 2 )
