摘要
支持向量机(SVM)是一种基于结构风险最小化原理、具有很高泛化性能的学习算法,为小样本、非线性、高维数一类信息融合问题的建模提供了一种有效的途径。本文将Mobile Agent运用到信息融合系统中,对信息融合系统中原有OODA模型进行改进,提出了一种基于SVM的Mobile Agent信息融合模型及算法。相关实验表明,本文中的训练算法可达到更为满意的分类效果,并且可以得到较高的分类精度。
The support vector machine （SVM） is an algorithm based on structure risk minimizing principle and has high gener-alization ability. The model offers a kind of effective way for the information fusion problem of little sample, non-linear and high dimension. In this paper, mobile agent is applied to information fusion system. The model of OODA and the study method of information fusion system are improved. The model and an algorithm of information fusion based on the support vector machine are proposed. The experiment results show that this hierarchical and parallel SVM training algorithm is efficient to deal with large-scale classification problems and has more satisfying accuracy in classification precision.
出处
《计算机科学》
CSCD
北大核心
2008年第3期191-193,共3页
Computer Science
基金
国家自然科学基金(60673131)
黑龙江省自然科学基金(F2005-02)
关键词
支持向量机
移动AGENT
信息融合
Support vector machine（SVM）,Mobile agent,Information fusion
