DocumentCode
2702882
Title
Comparing different clustering techniques-RBF networks training
Author
Brizzotti, M.M. ; de Carvalho, A.C.P.L.F.
Author_Institution
Dept. de Ciencias de Comput. e Estatistica, Sao Paulo Univ., Brazil
fYear
2000
fDate
2000
Firstpage
225
Lastpage
230
Abstract
Clustering techniques have a strong influence on the performance achieved by RBF neural networks. The article compares the performance achieved by RBF networks using seven different clustering techniques. For such, different sizes of RBF networks are trained and tested using an automatic target recognition data set. The performances of these RBF networks using each clustering technique are compared and analyzed
Keywords
iterative methods; optimisation; pattern clustering; radial basis function networks; self-organising feature maps; tree searching; unsupervised learning; RBF networks training; automatic target recognition data set; clustering techniques; Automatic testing; Computer networks; Image analysis; Information science; Interpolation; Neural networks; Performance analysis; Radial basis function networks; Target recognition; Unsupervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. Proceedings. Sixth Brazilian Symposium on
Conference_Location
Rio de Janeiro, RJ
ISSN
1522-4899
Print_ISBN
0-7695-0856-1
Type
conf
DOI
10.1109/SBRN.2000.889743
Filename
889743
Link To Document