Robust fuzzy clustering neural network based on ε-insensitive loss function

被引：12
|
作者：
Wang, Shitong [1 ]
Chung, Korris F. L.
Deng Zhaohong
Hu Dewen
机构：
[1] So Yangtze Univ, Sch Informat, WuXi, Jiang Su, Peoples R China
[2] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China
[3] Natl Def Univ Sci & Technol, Sch Automat, Changsha, Peoples R China
[4] Nanjing Univ, Natl Keysoft Lab, Nanjing 210008, Peoples R China
来源：
APPLIED SOFT COMPUTING | 2007年 / 7卷 / 02期
基金：
中国国家自然科学基金;
关键词：
fuzzy clustering; neural networks; epsilon-insensitive loss function; outliers; robustness;
D O I：
10.1016/j.asoc.2006.04.008
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In the paper, as an improvement of fuzzy clustering neural network FCNN proposed by Zhang et al., a novel robust fuzzy clustering neural network RFCNN is presented to cope with the sensitive issue of clustering when outliers exist. This new algorithm is based on Vapnik's einsensitive loss function and quadratic programming optimization. Our experimental results demonstrate that RFCNN has much better robustness for outliers than FCNN. (c) 2006 Elsevier B. V. All rights reserved.
引用
收藏
页码：577 / 584
页数：8
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