陈鹏, 刘爽, 左莉, 李锡祚. 基于数据分布规律的分段组合支持向量机研究[J]. 微电子学与计算机, 2015, 32(3): 94-99.
引用本文: 陈鹏, 刘爽, 左莉, 李锡祚. 基于数据分布规律的分段组合支持向量机研究[J]. 微电子学与计算机, 2015, 32(3): 94-99.
CHEN Peng, LIU Shuang, ZUO Li, LI Xi-zuo. Research on Piecewise Combination Support Vector Machine Based on Data Distribution Rules[J]. Microelectronics & Computer, 2015, 32(3): 94-99.
Citation: CHEN Peng, LIU Shuang, ZUO Li, LI Xi-zuo. Research on Piecewise Combination Support Vector Machine Based on Data Distribution Rules[J]. Microelectronics & Computer, 2015, 32(3): 94-99.

基于数据分布规律的分段组合支持向量机研究

Research on Piecewise Combination Support Vector Machine Based on Data Distribution Rules

  • 摘要: 在分析数据分布规律的基础上,提出了分段组合支持向量机算法.该算法首先统计数据分布规律,采用k均值聚类计算聚类中心,然后分球形分布、线性二分可分、局部线性二分可分三种情况来组织最小包含最大排除球目标函数、普通核函数、局部线性核函数,从而实现对不同的数据分布采用不同的支持向量机分类决策算法,提高算法的分类性能.

     

    Abstract: Based on analysis of data distribution rules, piecewise combination support vector machine is put forward in this paper. First, statistical analysis is adopted for the original data. Then, k-means cluster algorithm is introduced to compute cluster centers for the data. For three cases of data distribution, which is, sphere-distribution, linearly binary separable distribution, and locally linearly separable distribution, minimum enclosing and maximum excluding support vector machine, common support vector machine and local linear support vector machine are defined to solve the objective problems. Different support vector machines for different data distribution will improve classification performance.

     

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