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基于特征值分解的最大间隔支持向量回归机

, PP. 1817-1821

Keywords: 支持向量回归机,广义特征值中心支持向量机,非平行不敏感函数,特征值分解

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Abstract:

广义特征值中心支持向量回归机(GEPSVR)是一种有效的核回归算法,但其在求解优化问题时易导致奇异性问题.为此,提出一种基于特征值分解的支持向量回归机,简称IGEPSVR.与GEPSVR相比,IGEPSVR的主要优势有:结合最大间隔准则和GEPSVR几何思想给出了新的距离度量准则;在优化模型中引入Tikhonov正则项,克服了可能产生的奇异性问题;IGEPSVR仅需求解两个标准特征值,降低了计算复杂度.实验结果表明,较GEPSVR算法,IGEPSVR不仅提高了学习能力,而且缩短了训练时间.

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