• 研究论文 • 上一篇    下一篇



  1. (西安电子科技大学 智能信息处理研究所,陕西 西安 710071)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-02-20 发布日期:2007-02-25

Improvement classification performance by the support vector machine ensemble

LI Qing;JIAO Li-cheng

  1. (Research Inst. of Intelligent Information Processing, Xidian Univ., Xi′an 710071, China)
  • Received:1900-01-01 Revised:1900-01-01 Online:2007-02-20 Published:2007-02-25

摘要: 为了提高支撑矢量机的泛化性能,利用l倍交叉筛选和控制样本特征属性策略建立了集成支撑矢量机,该集成策略加强了子分类器之间的互异性,进一步提高了集成学习机的分类性能,提高了学习机的泛化性能,同时具有较好的鲁棒性.

关键词: 集成方法, 支撑矢量机, 集成支撑矢量机, 模式识别


Ensemble Methods are learning algorithms that construct a collection of individual classifiers which are independent and yet accurate, and then classify a new data point by taking vote of their predictions. The support Vector Machine (SVM) presents excellent performance in solving the problems with a small number of simple, nonlinear and local minima. The combination of the Support Vector Machine with Ensemble methods has been done by Hyun-Chul Kim based on the bagging algorithm, yet it does not show high robustness for its randomicity. In this paper, by a deep investigation into the principle of the SVM and the Ensemble Method, we propose two possible ways, cross validated committees and manipulating of the input feature strategies, to construct the SVM ensemble, which provides strong robustness according to experimental results.

Key words: ensemble method, support vector machine, SVM ensemble, pattern recognition


  • TP301