NDLI: A formula of equations of states in singular learning machines

Content Provider	IEEE Xplore Digital Library
Author	Watanabe, S.
Copyright Year	2008
Description	Author affiliation: PI Lab., Tokyo Inst. of Technol., Yokohama (Watanabe, S.)
Abstract	Almost all learning machines used in computational intelligence are not regular but singular statistical models, because they are nonidentifiable and their Fisher information matrices are singular. In singular learning machines, neither the Bayes a posteriori distribution converges to the normal distribution nor the maximum likelihood estimator satisfies the asymptotic normality, resulting that it has been difficult to estimate generalization performances. In this paper, we establish a formula of equations of states which holds among Bayes and Gibbs generalization and training errors, and show that two generalization errors can be estimated from two training errors. The equations of states proved in this paper hold for any true distribution, any learning machine, and a priori distribution, and any singularities, hence they define widely applicable information criteria.
Starting Page	2098
Ending Page	2105
File Size	244506
Page Count	8
File Format	PDF
ISBN	9781424418206
ISSN	10987576
DOI	10.1109/IJCNN.2008.4634086
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-06-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning Training Mathematical model Equations Hidden Markov models Maximum likelihood estimation Artificial neural networks
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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