Minimization of Empirical Risk as a Means of Choosing the Number of Hypotheses in Algebraic Machine Learning

Author:

Vinogradov D. V.

Publisher

Pleiades Publishing Ltd

Subject

Computer Vision and Pattern Recognition

Reference8 articles.

1. JSM-method of Automatic Generation of Hypotheses. Logical and Epistemological Foundations, Ed. by V. K. Finn and O. M. Anshakov (URSS, Moscow, 2009).

2. L. A. Iakimova, “The investigation of overfitting in algebraic machine learning,” Pattern Recognit. Image Anal. (2023).

3. S. O. Kuznetsov, “A fast algorithm for computing all intersections of objects in a finite semi-lattice,” Autom. Doc. Math. Linguist. 27 (5), 11–21 (1993).

4. V. N. Vapnik and A. Ya. Chervonenkis, Pattern Recognition Theory (Nauka, Moscow, 1974). In Russian

5. D. V. Vinogradov, “The rate of convergence to the limit of the probability of encountering an accidental similarity in the presence of counter examples,” Autom. Doc. Math. Linguist. 52, 35–37 (2018). https://doi.org/10.3103/s0005105518010090

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