Text Categorization Using SVM with Exponent Weighted ACO

摘要：

Support Vector Machine is a powerful tool for non-linear high-dimensional classification problem such as text categorization. Parameters include balance parameter C and kernel function parameter o play important roles in Support Vector Machine. However, classic method which selects parameters manually will restrict the improvement of classifying performance when using Support Vector Machine. This article proposes an exponent weighted algorithm to overcome local optimization and low convergence rate problems in Ant Colony Optimization. The novel Ant Colony Optimization algorithm is implemented and be used to optimizing parameters of Support Vector Machine in a Chinese text categorization system. The experimental results reveal this method has a higher precision and efficiency than traditional Support Vector Machine based Text categorization systems.

关键词： Support Vector Machine exponent weighted Ant Colony Optimization parameter selection text categorization

作者: LA Lei GUO Qiao

作者单位: School of Automation, Beijing Institute of Technology, Beijing 100081

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 3763-3768

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）