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基于类中心的SVM训练样本集缩减改进策略

DOI: 10.3969/j.issn.1674-0696.2014.02.35, PP. 154-158

Keywords: 信息技术,SVM,类中心,边界样本,替代策略,informationtechnology,SupportVectorMachine(SVM),class-center,boundarysample,alternativestrategy

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Abstract:

:?针对SVM训练样本集规模较大引发的学习速度慢、存储需求量大、泛化能力降低等问题,通过改进的样本点到类中心的方法来确定边界样本,从而大量缩减训练样本,提高训练速度。此外,针对非线性空间无法直接通过计算得到特征空间类中心的问题,提出了一种通过在特征空间中,寻找能生成最小超球的样本点来近似代替特征样本的替代策略,使得在保证分类精度的同时,提高了训练速度。

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