Abstract:
The simulation analysis and comparisons of the function approximation by SVM and NN are done in this paper, the results indicate that SVM has better performance than NN in the dependence on samples, insensitivity to noise and character of generalization with small-scale samples
Key words:
Support vector machine(SVM); Statistic learning theory; Neural network; Function approximation
摘要： 通过仿真分析比较支持向量机与前馈神经网络在非线性函数逼近的结果，得出在小样本下，SVM 的样本依赖程度、抗噪声能力和泛化性能都优于前馈神经网络。
关键词:
支持向量机；统计学习理论；神经网络；函数逼近
DU Xinhua, CHEN Zengqiang, YUAN Zhuzhi. Function Approximation Research Based on Support Vector Machine[J]. Computer Engineering, 2006, 32(8): 52-54,58.
杜新华，陈增强，袁著祉. 基于支持向量机函数逼近的性能研究[J]. 计算机工程, 2006, 32(8): 52-54,58.