摘要
提出一种快速的支持向量机分类算法———FCSVM ,对支持向量集采用变换的方式 ,用少量的支持向量代替全部支持向量进行分类计算 ,在保证不损失分类精度的前提下使得分类速度有较大提高 在UCI标准数据集上进行的分类实验以及在FERET标准人脸库上进行的人脸识别实验都表明该算法具有较好的性能 ,在一定程度上克服了传统的支持向量机分类速度较慢的缺点、尤其在训练集规模庞大、支持向量数量较多的情况下 ,采用该算法能够较大幅度地减小计算复杂度 。
Proposed in this paper is a fast classification algorithm of support vector machines—FCSVM (fast classification for support vector machines). After the transformation on the full set of support vectors, a subset of support vectors, which contains fewer support vectors, is used in classification. The speed of classification is much faster than that of conventional SVMs in the condition that the correct rate does not decline. The classification experiments on the UCI database and the face recognition experiments on the FERET face database are done with this algorithm. The experimental results show that it has better performance and partly overcomes the flaw of standard SVMs, which is slow in the process of classification. This algorithm can remarkably reduce the computation and increase the speed of classification, especially in the case of large number of support vectors.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2004年第8期1327-1332,共6页
Journal of Computer Research and Development
基金
国家自然科学基金项目 ( 60 2 73 0 3 3 )
江苏省十五科技攻关项目 (BE 2 0 0 10 2 8)
关键词
支持向量机
快速算法
分类
FCSVM
support vector machine
fast algorithm
classification
FCSVM
