基于Fisher鉴别分析的支持向量机训练样本缩减策略  

Sample reduction strategy for support vector machines based on fisher discriminant analysis

作　　者：饶刚[1] 刘琼荪[1] 

机构地区：[1]重庆大学数学与统计学院,重庆401331

出　　处：《计算机工程与应用》2012年第3期156-157,191,共3页Computer Engineering and Applications

基　　金：中央高校基本科研业务费资助(No.CDJXS11100049)

摘　　要：提出一种用于支持向量机训练样本集的缩减策略。该策略运用Fisher鉴别分析方法快速地提取潜在的支持向量,并构成用于SVM的新的训练样本集。仿真实验表明,该算法能在保证不降低分类精度的前提下,对较大规模的样本进行有效的缩减,提高运算效率。The paper presents a strategy of reducing the size of the training sample set for Support Vector Machines（SVM）.This strategy extracts the potential support vectors using the method of Fisher discriminant analysis,which forms the new training sample set used in SVM.The results of simulation experiments show effective reduction for large-scale training sample set and improvement of operation efficiency of this algorithm,guaranteeing the classification precision.

关 键 词：FISHER鉴别分析 投影 支持向量机 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]