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
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;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.836239
