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Statistical Mechanical Analysis of Simultaneous Perturbation Learning

Seiji MIYOSHI, Hiroomi HIKAWA, Yutaka MAEDA

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Summary :

We show that simultaneous perturbation can be used as an algorithm for on-line learning, and we report our theoretical investigation on generalization performance obtained with a statistical mechanical method. Asymptotic behavior of generalization error using this algorithm is on the order of t to the minus one-third power, where t is the learning time or the number of learning examples. This order is the same as that using well-known perceptron learning.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E92-A No.7 pp.1743-1746
Publication Date
2009/07/01
Publicized
Online ISSN
1745-1337
DOI
10.1587/transfun.E92.A.1743
Type of Manuscript
LETTER
Category
Neural Networks and Bioengineering

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