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基于尺度核函数的最小二乘支持向量机*

, PP. 598-603

Keywords: 支持向量机,核函数,支持向量核函数,尺度核函数,最小二乘支持向量机(LSSVM)

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Abstract:

支持向量机的核函数一直是影响其学习效果的重要因素.本文基于小波分解理论和支持向量机核函数的条件,提出一种多维允许支持向量尺度核函数.该核函数不仅具有平移正交性,且可以以其正交性逼近二次可积空间上的任意曲线,从而提升支持向量机的泛化性能.在尺度函数作为支持向量核函数的基础之上,提出基于尺度核函数的最小二乘支持向量机(LSSSVM).实验结果表明,LSSSVM在同等条件下比传统支持向量机的学习精度更高,因而更适用于复杂函数的学习问题.

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