| 支持向量机与RBF神经网络回归性能比较研究 |
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| 引用本文： | 刘苏苏,孙立民. 支持向量机与RBF神经网络回归性能比较研究[J]. 计算机工程与设计, 2011, 32(12): 4202-4205 |
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| 作者姓名： | 刘苏苏 孙立民 |
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| 作者单位： | 烟台大学计算机学院,山东烟台,264005 |
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| 基金项目： | 山东省自然科学基金项目 |
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| 摘 要： | 支持向量机与RBF神经网络相比各有优缺点,通过对支持向量机与RBF神经网络的研究,从理论上分析了这两种学习机在回归预测原理上的异同,通过仿真实验对比了两者在测试集上的逼近能力及泛化能力。仿真结果表明,对于小样本集,支持向量机的逼近能力及泛化能力要优于RBF神经网络。对实际应用中回归模型的选择问题提出了建议。
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| 关 键 词： | 支持向量机 RBF神经网络 逼近能力 泛化能力 回归 |
Performance comparison of regression prediction on support vector machine and RBF neural network |
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| LIU Su-su,SUN Li-min. Performance comparison of regression prediction on support vector machine and RBF neural network[J]. Computer Engineering and Design, 2011, 32(12): 4202-4205 |
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| Authors: | LIU Su-su SUN Li-min |
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| Affiliation: | LIU Su-su,SUN Li-min(School of Computer Science,Yantai University,Yantai 264005,China) |
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| Abstract: | The support vector machine(SVM) and RBF neural network have their respective advantages and defects while using in regression prediction.By study of SVM and RBF natural network,the paper firstly analyzes the two kinds of learning machines used in regression theoretically.And secondly,the experiments are done to compare their approximation and generalization abilities.The experiment results show that the approximation and generalization abilities of SVM are better than RBF neural network for the small datase... |
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| Keywords: | SVM RBF neural network approximation generalization regression |
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