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Estimation of Overfitting Degree of Algebraic Machine Learning in Boolean Algebra

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Abstract

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.

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ACKNOWLEDGMENTS

The author is grateful to his colleagues at the Dorodnicyn Computing Center, Federal Research Center Computer Science and Control, Russian Academy of Sciences, for support and useful discussions. Special thanks to L.A. Yakimova for collaboration, discussion, and support.

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  1. Federal Research Center Computer Science and Control, Russian Academy of Sciences, Moscow, Russia

    D. Vinogradov

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  1. D. Vinogradov
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Correspondence to D. Vinogradov.

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Vinogradov, D. Estimation of Overfitting Degree of Algebraic Machine Learning in Boolean Algebra. Autom. Doc. Math. Linguist. 56, 160–162 (2022). https://doi.org/10.3103/S0005105522030098

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  • DOI: https://doi.org/10.3103/S0005105522030098

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