DocumentCode
1915194
Title
A self generating neural architecture for data analysis
Author
Alahakoon, L.D. ; Halgamuge, S.K. ; Srinivasan, B.
Author_Institution
Sch. of Comput. Sci. & Software Eng., Monash Univ., Clayton, Vic., Australia
Volume
5
fYear
1999
fDate
1999
Firstpage
3548
Abstract
Supervised and unsupervised self generating neural network architectures have been used in the recent past. Our previous work (1998) has described an unsupervised self generating feature map, called the growing self organising map (GSOM). In this paper we describe some extensions to the GSOM such that it could be used to map and analyse more realistic data sets
Keywords
data analysis; learning (artificial intelligence); neural net architecture; self-organising feature maps; data analysis; data mining; growing self organising map; learning rate; self generating neural network; Australia; Computer architecture; Data analysis; Data mining; Euclidean distance; Manufacturing; Neural networks; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-5529-6
Type
conf
DOI
10.1109/IJCNN.1999.836239
Filename
836239
Link To Document