Journal of South China University of Technology (Natural Science Edition) ›› 2008, Vol. 36 ›› Issue (9): 1-5.

• Computer Science & Technology •     Next Articles

Support Vector Machine for Classification Based on Cognitive Geometry

Wen Gui-hua  Zhu Jin-feng  Lu Ting-hui   

  1. School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, Guangdong, China
  • Received:2007-10-12 Revised:2007-11-16 Online:2008-09-25 Published:2008-09-25
  • Contact: 文贵华（1968－），男，博士，副研究员，主要从事创新计算、机器认知、机器学习与数据挖掘研究． E-mail:crghwen@seut．edu．cn
  • About author:文贵华（1968－），男，博士，副研究员，主要从事创新计算、机器认知、机器学习与数据挖掘研究．
  • Supported by:

    广东省科技攻关项目（20078030803006）;湖北省科技攻关项目（2005AA101C17）

PDF

827

Abstract:

School of Computer Science and Engineering, South China University of Technology, Guangzhou 510640, Guangdong, ChinaAbstract:Although a support vector machine （SVM） has excellent classification ability for small data sets, it is still inefficient for noisy or sparse data sets. As humans can effectively deal with noisy and sparse data, a geometric approach of modeling human＇s cognitive abilities is proposed in this paper. Moreover, a geometric model of the relative cognitive law is established via relative transformation and is then used to improve SVM. It is indicated from the simulation that the classification capability of the improved SVM for noisy and sparse data sets significantly increases.

Key words: support vector machine, cognitive law, relative transformation, cognitive geometry