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基于支持向量机的曲线重建方法
引用本文:王国锋,刘岩,李言俊.基于支持向量机的曲线重建方法[J].西北工业大学学报,2004,22(1):33-36.
作者姓名:王国锋  刘岩  李言俊
作者单位:西北工业大学,航天学院,陕西,西安,710072
基金项目:航天科技创新基金,西北工业大学博士论文创新基金资助
摘    要:基于统计学习理论(SLT)的支持向量机(SVM)在高维空间中表示复杂函数是一种有效的通用方法,也是一种新的、很有发展前景的机器学习算法。文中简要介绍了基于支持向量机的理论,并在此基础上提出了一种基于支持向量机(SVM)的曲线重建算法,最后给出了实验,证明了该方法的有效性。

关 键 词:支持向量机  曲线重建  函数拟合逼近  神经网络  统计学习理论  机器学习
文章编号:1000-2758(2004)01-0033-04
修稿时间:2002年4月12日

Curve Reconstruction Based on Support Vector Machine
Wang Guofeng,Liu Yan,Li Yanjun.Curve Reconstruction Based on Support Vector Machine[J].Journal of Northwestern Polytechnical University,2004,22(1):33-36.
Authors:Wang Guofeng  Liu Yan  Li Yanjun
Abstract:The existing method of neural network for function approaching in curve reconstruction suffers from the shortcoming of local minimum and slow convergence rate. We present a curve reconstruction algorithm based on SVM theory. This algorithm can improve the standardization ability of its learning machine according to the principle of minimal structure risk. This algorithm also avoids the iterative operation of BP learning algorithm, thus its convergence rate is faster than the BP algorithm. The advantages of this algorithm are that it requires less samples to reconstruct a curve, and its approaching precision is higher. In addition, it ensures that the extremum is global. Simulation result as given in Section 2 shows preliminarily that this algorithm is effective.
Keywords:curve reconstruction algorithm  support vector machine(SVM)  function approaching
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