Abstract—
The paper examines a new approach to assessing the number of required hypotheses about the causes of a target property. It follows the classical method of V.N. Vapnik and A.Y. Chervonenkis—minimization of the number of classification errors on the training sample. However, there is a very close analogy with the procedure of an abductive explanation of the training sample by V.K. Finn.
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ACKNOWLEDGMENTS
The author thanks his colleagues at the Dorodnicyn Computing Center of the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences for support and useful discussions. Special appreciation is expressed to L.A. Iakimova for the joint work, discussion, and support.
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Dmitry V. Vinogradov is a Leading Research Fellow at the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences.
In 1986, he graduated from the Faculty of Mechanics and Mathematics of the Lomonosov Moscow State University.
In 2019, he earned a Doctor of Science degree in Theoretical Computer Science.
His research areas are probabilistic algorithms, Markov chains, machine learning, lattice theory.
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Vinogradov, D.V. Minimization of Empirical Risk as a Means of Choosing the Number of Hypotheses in Algebraic Machine Learning. Pattern Recognit. Image Anal. 33, 525–528 (2023). https://doi.org/10.1134/S1054661823030458
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DOI: https://doi.org/10.1134/S1054661823030458