DocumentCode
1749088
Title
General properties of the generalised Lotto-type competitive learning
Author
Luk, Andrew ; Lien, Sandra
Author_Institution
St B&P Neural investments Pty Ltd., Australia
Volume
1
fYear
2001
fDate
2001
Firstpage
418
Abstract
We highlight the difficulty of detecting small and rare clusters. Although in theory it is possible for the algorithms to follow the source density function, we note that in experiment it is very difficult to locate these smaller clusters, especially if the prototype set is limited. We propose to train the network iteratively with the same prototype set. This simple method enable one to locate these smaller clusters, albeit with the possibility of over-training
Keywords
iterative methods; neural nets; unsupervised learning; Lotto-type learning; clusters; competitive learning; iterative method; source density function; Australia; Clustering algorithms; Convergence; Density functional theory; Frequency; Investments; Iterative algorithms; Neural networks; Neurons; Prototypes;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.939056
Filename
939056
Link To Document