东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2010, Vol. 31 ›› Issue (1): 16-19.DOI: -

• 论著 • 上一篇    下一篇

基于超球的支持向量机增量学习算法

徐喆;毛志忠;   

  1. 东北大学信息科学与工程学院;东北大学流程工业综合自动化教育部重点实验室;
  • 收稿日期:2013-06-20 修回日期:2013-06-20 出版日期:2010-01-15 发布日期:2013-06-20
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家高技术研究发展计划项目(2007AA041401;2007AA04Z194)

Incremental learning of support vector machine based on hyperspheres

Xu, Zhe (1); Mao, Zhi-Zhong (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China; (2) Key Laboratory of Integrated Automation of Process Industry, Northeastern University, Shenyang 110004, China
  • Received:2013-06-20 Revised:2013-06-20 Online:2010-01-15 Published:2013-06-20
  • Contact: Xu, Z.
  • About author:-
  • Supported by:
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摘要： 支持向量机方法已经成功地应用于解决分类和回归问题,但是在训练支持向量机时需要求解二次规划问题,使得支持向量机的训练时间过长,训练样本量越大,这个缺陷越明显.将超球方法与回归支持向量机相结合,提出一种增量学习的新方法.该方法使用两个同心超球缩减训练集,以达到提高训练速度的目的.通过分析表明,这种新的增量学习方法较普通支持向量机训练方法有较低的计算复杂度.实验结果表明,该算法可以在不降低预测准确性的同时减少大量建模时间.

关键词: 增量学习, 支持向量机, 二次规划, 超球, 回归

Abstract: Support vector machine (SVM) has been successfully applied to solving classification and regression problems. However, to solve the quadratic programming problem is needed for SVM training process, and the more the number of training samples, the longer the training process. A novel incremental learning method was therefore proposed combining the hypershpere approach with regressional SVM to reduce the number of training samples by using two concentric hypershperes, thus shortening the training time. Analysis results showed that the incremental learning method has lower computational complexity than the normal SVM training method. Experimental results demonstrated that the method proposed can dramatically save the training time with negaligible degradation of regression accuracy.

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