Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11678
Title: Robust fuzzy clustering neural network based on epsilon-insensitive loss function
Authors: Wang, S
Chung, KFL 
Deng, Z
Hu, D
Keywords: Fuzzy clustering
Neural networks
Epsilon-insensitive loss function
Outliers
Robustness
Issue Date: 2007
Publisher: Elsevier
Source: Applied soft computing, 2007, v. 7, no. 2, p. 577-584 How to cite?
Journal: Applied soft computing 
Abstract: 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.
URI: http://hdl.handle.net/10397/11678
ISSN: 1568-4946
EISSN: 1872-9681
DOI: 10.1016/j.asoc.2006.04.008
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

11
Last Week
0
Last month
0
Citations as of Jun 1, 2018

WEB OF SCIENCETM
Citations

8
Last Week
0
Last month
0
Citations as of Jun 12, 2018

Page view(s)

48
Last Week
5
Last month
Citations as of Jun 17, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.