Adaptive local hyperplane classification

被引：42
|
作者：
Yang, Tao [1 ]
Kecman, Vojislav [1 ]
机构：
[1] Univ Auckland, Sch Engn, Auckland 1, New Zealand
来源：
NEUROCOMPUTING | 2008年 / 71卷 / 13-15期
关键词：
pattern classification; nearest neighbor; manifold;
D O I：
10.1016/j.neucom.2008.01.014
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a novel classifier, called adaptive local hyperplane, is proposed for pattern classification. The experimental results on 11 real data sets demonstrate that the proposed classifier outperforms, on average, all the other seven benchmarking classifiers. In particular, it is the best classifier in 10 out of 11 data sets, and it is the close second best for just one data set. (c) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码：3001 / 3004
页数：4
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