Recognition and Geometrical On-line Learning Algorithm of Probability Distributions

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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3

Como, Italy

July 24-July 27

ISBN: 0-7695-0619-4

	ASCII Text	x
	Toshiaki Aida, "Recognition and Geometrical On-line Learning Algorithm of Probability Distributions," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 3, pp. 3175, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000.

	BibTex	x
	@article{ 10.1109/IJCNN.2000.861300, author = {Toshiaki Aida}, title = {Recognition and Geometrical On-line Learning Algorithm of Probability Distributions}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {3}, year = {2000}, issn = {1098-7576}, pages = {3175}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861300}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on TI - Recognition and Geometrical On-line Learning Algorithm of Probability Distributions SN - 1098-7576 SP EP A1 - Toshiaki Aida, PY - 2000 VL - 3 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER -

Toshiaki Aida, Tokyo Institute of 'Technology

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861300

An on-line 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 non-covariant case.

Citation:

Toshiaki Aida, "Recognition and Geometrical On-line Learning Algorithm of Probability Distributions," ijcnn, vol. 3, pp.3175, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000

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