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支持向量机训练算法的比较研究

The Comparative Research on Support Vector Machine Training Algorithm
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摘要 介绍了支持向量机的数学模型,重点论述了两种比较典型的分解算法,即SVMlight和SMO,并对这两种算法的优点和缺点进行了分析和总结。 This paper introduces the mathematical model of support vector machine, expounds two typical decomposition algorithms, SVMlight and SMO, and analyzes and sums up the advantages and disadvantages of these two algorithms.
作者 俞胜益 付燕
机构地区 西安科技大学计算机学院
出处 《科技情报开发与经济》 2008年第30期138-140,共3页 Sci-Tech Information Development & Economy
关键词 支持向量机 SVMlight SMO support vector machine SVMlight SMO
分类号 TP391.6 [自动化与计算机技术—计算机应用技术]
  • 相关文献

参考文献4

  • 1Cristianini N, Shawe-Taylor J.lntroduction to Support Vector Machines [M].Cambridge: Cambridge University Press,2000:52-76.
  • 2Corinna Cortes,Vladimir Vapnik.Support Vector Networks[J].Machine Learning, 1995,20 ( 3 ):273-297.
  • 3萧嵘,王继成,张福炎.支持向量机理论综述[J].计算机科学,2000,27(3):1-3. 被引量：35
  • 4KeerthiS,ShevadeS,Bhattchayyra C,et al.Improvements to Platt's algorithm for SVM classifier design[J]. Neural Computation, 2001,13( 3 ):637-649.

二级参考文献10

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  • 6[6]Keerthi S S, el al. A fast iterative nearest point algorithm for support vector machine classifier design: [Technical Report No. TR-ISL-99-03]. Dept of CSA,IISC,Ranga lore, INDIA. 1999
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共引文献34

科技情报开发与经济
2008年 第30期

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