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Título
Classification and ICA using maximum likelihood Hebbian learning
Autor(es)
Palabras clave
Computer Science
Fecha de publicación
2002
Editor
IEEE
Citación
Neural Networks for Signal Processing, 2002. Proceedings of the 2002 12th IEEE Workshop on. pp. 327 - 336 .
Abstract
We investigate an extension of Hebbian learning in a principal component analysis network which has been derived to be optimal for a specific probability density function(PDF). We note that this probability density function is one of a family of PDFs and investigate the learning rules formed in order to be optimal for several members of this family. We show that, whereas previous authors have viewed the single member of the family as an extension of PCA, it is more appropriate to view the whole family of learning rules as methods of performing exploratory projection pursuit (EPP). We explore the performance of our method first in response to an artificial data type, then to a real data set.
URI
ISBN
0-7803-7616-1 (Print)
Collections
- BISITE. Congresos [397]