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基于CCH的SVM几何算法及其应用

彭新俊 王翼飞

彭新俊, 王翼飞. 基于CCH的SVM几何算法及其应用[J]. 应用数学和力学, 2009, 30(1): 90-100.
引用本文: 彭新俊, 王翼飞. 基于CCH的SVM几何算法及其应用[J]. 应用数学和力学, 2009, 30(1): 90-100.
PENG Xin-jun, WANG Yi-fei. CCH-Based Geometric Algorithms for SVM and the Applications[J]. Applied Mathematics and Mechanics, 2009, 30(1): 90-100.
Citation: PENG Xin-jun, WANG Yi-fei. CCH-Based Geometric Algorithms for SVM and the Applications[J]. Applied Mathematics and Mechanics, 2009, 30(1): 90-100.

基于CCH的SVM几何算法及其应用

基金项目: 国家自然科学基金资助项目(30571059);国家高科技研究发展计划(863)专项资助项目(2006AA02Z190);上海市重点学科资助项目(S30405)
详细信息
    作者简介:

    彭新俊(1980- ),男,湖南人,博士(联系人:E-mail:xjpeng@shnu.edu.cn);王翼飞(1948- ),男,教授,博士生导师(E-mail:yifei_wang@shu.edu.cn).

  • 中图分类号: O235;TP18

CCH-Based Geometric Algorithms for SVM and the Applications

  • 摘要: 支持向量机(support vector machine(SVM))是一种数据挖掘中新型机器学习方法.提出了基于压缩凸包(compressed convex hull(CCH))的SVM分类问题的几何算法.对比简约凸包(reduced convex hull(RCH)),CCH保持了数据的几何体形状,并且易于得到确定其极点的充要条件.作为CCH的实际应用,讨论了该几何算法的稀疏化方法及概率加速算法.数值试验结果表明所讨论的算法可降低核计算并取得较好的性能.
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出版历程
  • 收稿日期:  2008-08-26
  • 修回日期:  2008-11-17
  • 刊出日期:  2009-01-15

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