On a higher-order neural network for distortion invariant pattern recognition

被引：22
作者：
Kaita, T
Tomita, S
Yamanaka, J
机构：
[1] Oshima Natl Coll Maritime Technol, Informat Sci & Technol Dept, Yamaguchi 7422193, Japan
[2] Shobi Univ, Dept Humanit Informat, Fac Mus & Mediaarts, Kawagoe, Saitama 3501153, Japan
来源：
PATTERN RECOGNITION LETTERS | 2002年 / 23卷 / 08期
关键词：
decomposition effect; distortion invariant pattern recognition; training; higher-order neural network; local structure;
D O I：
10.1016/S0167-8655(02)00028-4
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper proposed some methods upon a second-order neural network. These networks apply the normalized frequency distribution of distance between two points on an object. 0.766 of the recognition accuracy for 2D two-class mixture distributions and 0.960 for hand-written characters are achieved. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：977 / 984
页数：8
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