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Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Print: 1064-2315
ISSN Online: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/J Automat Inf Scien.v38.i11.60
pages 56-73

Complexity of Bayesian Procedure of Inductive Inference. Discrete Case

Boris A. Beletskiy
V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, Kiev, Ukraine
Alexandra A. Vagis
V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, Kiev, Ukraine
Sergey V. Vasilyev
V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, Kiev, Ukraine
Nikita A. Gupal
V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, Kiev

ABSTRACT

Behavior of inductive procedures depending on content of learning sampling is studied. We demonstrate, that if the learning sampling contains no information about some class of objects or statistical information about a priori probabilities of classes, then any procedure works badly and its error is strictly positive. An estimate of error of Bayesian recognition procedure depending on size of learning sampling and other parameters is derived. Suboptimality of Bayesian approach is proved, complexity of class of problems is assessed.


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