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基于自适应边界向量提取的多尺度v-支持向量机建模

DOI: 10.13195/j.kzyjc.2014.0044, PP. 721-726

Keywords: 大样本建模,边界向量提取,多尺度学习,v-支持向量机

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Abstract:

针对??-支持向量机(v-SVM)用于大规模、多峰样本建模时易出现训练速度慢和回归精度低的问题,提出基于边界向量提取的多尺度v-SVM建模方法.该方法采用一种自适应边界向量提取算法,从训练样本中预提取出包含全部支持向量的边界向量集,以缩减训练样本规模,并通过求解多尺度v-SVM二次规划问题获取全局最优回归模型,从多个尺度上对复杂分布样本进行逼近.仿真结果表明,基于边界向量提取的多尺度v-SVM比v-SVM具有更好的回归结果.

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