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Improvements on sequential minimal optimization algorithm for support vector machine based on semi-sparse algorithm

  • Xiaopeng Yang*
  • , Hu Guan
  • , Feilong Tang
  • , Ilsun You
  • , Minyi Guo
  • , Yao Shen
  • *此作品的通讯作者
  • Shanghai Jiao Tong University
  • Korean Bible University

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

Sequential Minimal Optimization (SMO) is one of simple but fast iterative algorithm for Support Vector Machine (SVM), while there is a large amount of vector multiplication in SMO, which is still expensive and time-consuming. In this paper, we propose our Semi-sparse algorithm to enhance the vector multiplication in the SMO algorithms for large-scale sparse matrices. In the worst scenario, the traditional sparse algorithm on SMO needs O(n1+n2) times of judgments and addressing on two sparse vectors which own m and n elements respectively, while Semi-sparse algorithm can nearly finish this multiplying process within O(n2). Our experimental results on two benchmarks show that the modified SVMTorch based on our Semi-sparse algorithm can perform significantly faster than SVMTorch based on the original sparse algorithm.

源语言英语
主期刊名Proceedings - 2011 5th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2011
192-199
页数8
DOI
出版状态已出版 - 2011
已对外发布
活动2011 5th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2011 - Seoul, 韩国
期限: 30 6月 20112 7月 2011

出版系列

姓名Proceedings - 2011 5th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2011

会议

会议2011 5th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2011
国家/地区韩国
Seoul
时期30/06/112/07/11

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