文献类型：期刊文献

中文题名：一种改进的最小二乘支持向量机及其应用

英文题名：An Improved Least Squares Support Vector Machine and Its Applications

作者：余艳芳[1];高大启[1]

机构：[1]华东理工大学计算机科学与工程系,上海200237

年份：2006

卷号：28

期号：2

起止页码：69

中文期刊名：计算机工程与科学

外文期刊名：Computer Engineering & Science

收录：CSTPCD;;CSCD:【CSCD2011_2012】；

基金：国家自然科学基金资助项目(60275017;60373073);上海市重点科技攻关项目(025115028;04dz05010)

语种：中文

中文关键词：最小二乘支持向量机;序贯最小优化;自适应导向循环图

外文关键词：least squares support vector machine; sequential minimal optimization; adaptive directed aeyclic graph

摘要：为了克服传统支持向量机训练速度慢、计算资源需求大等缺点,本文应用最小二乘支持向量机算法来解决分类问题。同时,本文指出了决策导向循环图算法的缺陷,采用自适应导向循环图思想来实现多类问题的分类。为了提高样本的学习速度,本文还将序贯最小优化算法与最小二乘支持向量机相结合,最终形成了ADAGLSSVM算法。考虑到最小二乘支持向量机算法失去了支持向量的稀疏性,本文对支持向量作了修剪。实验结果表明,修剪后,分类器的识别精度和识别速度都得到了提高。
Conventional support vector machines （SVMs） have the demerits ot low training speect and htgn computauonal requirements. To overcome the shortcomings, this paper applies the least squares support vector machine （LSSVM） to the issue of pattern classification. Meanwhile, this paper briefly points out the limitations of the decision directed acyclic graph （DDAG） algorithm, and uses the adaptive directed acyclic graph （ADAG） algorithm for solving multiclass problems. In order to enhance the learning speed of samples, this paper introduces sequential minimal optimization （SMO） to LSSVM, and finally constructs the ADAGLSSVM algorithm. Owing to the lacking sparsity in the LSSVM algorithm, support vectors are pruned in the experiment to improve the accuracy and speed of classifiers. Experimental result shows that the performance of classifiers is improved after pruning.