计算机科学 ›› 2013, Vol. 40 ›› Issue (12): 104-107.

• 综述 • 上一篇    下一篇

商空间框架下的大规模SVM数据集约减法

覃希,苏一丹,张雯   

  1. 广西大学计算机与电子信息学院 南宁530004;广西大学计算机与电子信息学院 南宁530004;广西大学计算机与电子信息学院 南宁530004
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受中国国家自然科学基金(61063032),中国教育部人文社会科学研究规划基金(11YJAZH080)资助

Reduction for Large-scale SVM Datasets under Quotient Space

QIN Xi,SU Yi-dan and ZHANG Wen   

  • Online:2018-11-16 Published:2018-11-16

摘要: 借助商空间框架下的粒度分析理论及其计算方法,提出将“粒度”的概念用于大规模SVM数据集的约减来建立商空间框架下的约减模型。该约减模型的约减方向是由远及近地向分类超平面削减,其削减幅度也伴随集合的缩小而由粗到细逐渐变化。同时,给出该模型的一种实现。实验证明,商空间框架下的SVM约减模型比普通SVM约减模型的压缩效果更好。

关键词: 商空间,粒度,约减法,支持向量机

Abstract: Using granularity analysis theory and computational method in quotient space,we are able to build a reduction model in quotient space.In this model,we cut out the redundant data using variable granularity so that the reduction becomes more accurate.An example implementation of this method was provided.Experiments indicate our new method yields significantly improved compression without sacrificing the accuracy of traditional SVM techniques.

Key words: Quotient space,Granularity,Reduction,SVM

[1] Rychetsky M,Ortmann S,Ullmann M,et al.Accelerated Training of Support Vector Machines[C]∥International Joint Conference on Neural Networks,1999.IEEE,1999:998-1003
[2] Hsu C-W,Lin C-J.A Comparison of Methods for MulticlassSupportVector Machines[J].Neural Networks,2002,13(2):415-425
[3] Yu H,Yang Jiong,Han Jia-wei.Classifying Large Data Sets Using SVMs with Hierarchical Clusters[C]∥Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining,2003.New York:ACM,2003:306-315
[4] 奉国和,朱思铭.基于聚类的大样本支持向量机研究[J].计算机科学,2006,33(4):145-147
[5] Xu Yan-zi,Qin Hua.A New Optimazation Method of Large-scale SVMs Based on Kernel Distance clustering[C]∥Computational Intelligence and Software Engineering,2009.Wuhan:IEEE,2009
[6] Cevikalp H.New clustering algorithms for the support vectormachine based hierarchical classification[J].Pattern Recognition Letters,2010,1(11):1285-1291
[7] He Qiang,Xie Zong-xia,Hu Qing-hua.Neighborhood basedsample and feature selection for SVM classification learning[J].Neurocomputing,2011,74(10):1585-1594
[8] 胡正平,高文.基于改进加权压缩近邻与最近边界规则SVM训练样本约减选择算法[J].燕山大学学报,2010,4(5):421-425
[9] 张钹,张铃.粒计算未来发展方向探讨[J].重庆邮电大学学报:自然科学版,2010,2(5):538-540
[10] 郭庆文,王国胤,张清华.多粒度的图像检索方法研究[J].计算机科学,2013,40(1):298-301
[11] 程伟,石扬,张燕平.粒度计算的三种主要方法[J].计算机技术与发展,2007,7(3):91-94
[12] 李道国,苗夺谦,张红云.粒度计算的理论、模型与方法[J].复旦学报:自然科学版,2004,4(5):837-841
[13] 张玲,张钹.模糊商空间理论[J].软件学报,2003,4(4):770-776
[14] 覃希,苏一丹.用双层减样法优化大规模SVM垃圾标签检测模型[J].计算机应用研究,2011,8(6):2095-2098
[15] 覃希,夏宁霞,苏一丹.基于支持向量机的垃圾标签检测模型[J].计算机应用研究,2010,7(10):3893-3895

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