Estimation of Overfitting Degree of Algebraic Machine Learning in Boolean Algebra

The paper presents an estimation of overfitting probability for VKF-method of algebraic machine learning in the simplest case of Boolean algebra without counter-examples. The model uses the Vapnik—Chervonenkis proposal to minimize the empirical risk. Asymptotically the probability of overfitting err...

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Bibliographic Details
Published in:Automatic documentation and mathematical linguistics 2022-06, Vol.56 (3), p.160-162
Main Author: Vinogradov, D.
Format: Article
Language:English
Subjects:
Boolean algebra
Computer Science
Fractions
Information Storage and Retrieval
Intelligent Systems
Machine learning
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Summary:The paper presents an estimation of overfitting probability for VKF-method of algebraic machine learning in the simplest case of Boolean algebra without counter-examples. The model uses the Vapnik—Chervonenkis proposal to minimize the empirical risk. Asymptotically the probability of overfitting errors for a fixed fraction of test examples tends to zero faster than exponentially decrease if the description length and the number of requested hypotheses go to infinity.
ISSN:0005-1055
1934-8371
DOI:10.3103/S0005105522030098