Nonparallel least square support vector machine for classification

详细信息    查看全文

• 作者：Jiang Zhao ; Zhiji Yang ; Yitian Xu
• 关键词：Support vector machine ; Nonparallel hyper ; planes ; Intersection angle ; Least square
• 刊名：Applied Intelligence
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：45
• 期：4
• 页码：1119-1128
• 全文大小：605 KB
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Mechanical Engineering
  Manufacturing, Machines and Tools
  
• 出版者：Springer Netherlands
• ISSN：1573-7497
• 卷排序：45

文摘

Nonparallel support vector machine based on one optimization problem (NSVMOOP) aims at finding two nonparallel hyper-planes by maximizing the intersection angle of their normal vectors w₁ and w₂. As maximum intersection angle preserves both compactness and separation of data, NSVMOOP yields good forecasting accuracy. However, as it solves one large quadratic programming problem (QPP), it costs high running time. In order to improve its learning speed, a novel nonparallel least square support vector machine (NLSSVM) is proposed in this paper. NLSSVM solves a linear system of equations instead of solving one large QPP. As both intersection angle and least square version are applied on our NLSSVM, it performs better generalization performance than other algorithms. Experimental results on twenty benchmark datasets demonstrate its validity.