DocumentCode
353239
Title
Recognition and geometrical on-line learning algorithm of probability distributions
Author
Aida, Toshiaki
Author_Institution
Dept. of Phys., Tokyo Inst. of Technol., Japan
Volume
3
fYear
2000
fDate
2000
Firstpage
175
Abstract
An online learning algorithm for probability distributions is constructed in a reparameterization invariant form. It enables us to identify the distributions which transform from one to another by reparameterization. This is an essential property not only for pattern recognition problems but also for the property of `information´. We can find the algorithm to be optimal, since conformal gauge reduces the problem to a noncovariant case
Keywords
geometry; learning (artificial intelligence); neural nets; online operation; optimisation; pattern recognition; probability; conformal gauge; geometrical online learning algorithm; noncovariant case; optimal algorithm; pattern recognition; probability distributions; reparameterization invariant form; Aerospace engineering; Algorithm design and analysis; Control systems; Educational institutions; Inference algorithms; Learning systems; Pattern recognition; Physics; Probability distribution; Speech processing;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.861300
Filename
861300
Link To Document