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Discussion on the Relation Between SVM Training Sample Size and Correct Forecast Ratio for Simulation Experiment Results

机译:探讨SVM训练样本大小与仿真实验结果预测率的关系

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摘要

A series of support vector machine (SVM) forecast experiments are carried out to reveal the relation between the SVM training sample size and SVM correct forecast ratio for simulation experiment results. Experiment results show that the SVM correct forecast ratio increases to some extent with the number of training samples becoming more and then keeps unchanged even if the SVM training sample number increases further. And SVM has also been proved to be able to overcome the over-fitting issue always afflicting BackPropagation Neural Networks (BPNN).
机译:进行了一系列支持向量机(SVM)预测实验,以揭示SVM训练样本大小与SVM正确预测率之间的关系进行仿真实验结果。实验结果表明,SVM正确的预测比率在一定程度上增加,训练样本的数量也变得更多,即使SVM训练样本数进一步增加,也会保持不变。还证明了SVM能够克服过度拟合的问题,始终折磨后代神经网络(BPNN)。

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