DocumentCode :
1956671
Title :
RBF network with two-stage supervised learning: an application
Author :
Blonda, P. ; Baraldi, A. ; Addabbo, A.D. ; Tarantino, C.
Author_Institution :
CNR-IESI, Bari, Italy
fYear :
2002
fDate :
2002
Firstpage :
129
Lastpage :
133
Abstract :
In the field of image classification, this paper compares a traditional RBF two-stage hybrid learning procedure with an RBF two-stage learning technique exploiting labeled data to adapt hidden unit parameters. RBF centers are determined by running a clustering algorithm separately on different training sets, where each set is associated with a different class. The ELBG neural network is used as clustering algorithm. Two different data sets have been considered. The first consists of real three Synthetic Aperture Radar (SAR) image tandem pairs from ERS1/ERS2 satellites. The second consists of Magnetic Resonance (MR) slices of a patient affected by multiple sclerosis. The results indicate that the supervised approach performs better than the traditional approach when the number of hidden unit is the same and seems more stable to changes in the number of hidden units.
Keywords :
image classification; learning (artificial intelligence); pattern clustering; radial basis function networks; ELBG neural network; Enhanced Linde-Buzo-Gray algorithm; RBF; batch learning; clustering; clustering algorithm; image classification; labeled data; learning; Clustering algorithms; Data mining; Lesions; Magnetic resonance; Multiple sclerosis; Neural networks; Radial basis function networks; Supervised learning; Synthetic aperture radar; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Information Processing Society, 2002. Proceedings. NAFIPS. 2002 Annual Meeting of the North American
Print_ISBN :
0-7803-7461-4
Type :
conf
DOI :
10.1109/NAFIPS.2002.1018042
Filename :
1018042
Link To Document :
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