Comparing Linear Discriminant Analysis and Support Vector Machines

被引：0
|
作者：
Gokcen, I [1 ]
Peng, J [1 ]
机构：
[1] Tulane Univ, Dept EECS, New Orleans, LA 70118 USA
来源：
ADVANCES IN INFORMATION SYSTEMS | 2002年 / 2457卷
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Both Linear Discriminant Analysis and Support Vector Machines compute hyperplanes that are optimal with respect to their individual objectives. However, there can be vast differences in performance between the two techniques depending on the extent to which their respective assumptions agree with problems at hand. In this paper we compare the two techniques analytically and experimentally using a number of data sets. For analytical comparison purposes, a unified representation is developed and a metric of optimality is proposed.
引用
收藏
页码：104 / 113
页数：10
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