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Search results for “number-theory”

29 results

IMO 2011

2026Problem sheet

52nd International Mathematical Olympiad. Amsterdam, 2011.
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IMO 2017

2026Problem sheet

58th International Mathematical Olympiad. Rio de Janeiro, Brazil, 2017.
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IMO 2024

2026Problem sheet

65th International Mathematical Olympiad, 2024.
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IMO 2021

2026Problem sheet

62nd International Mathematical Olympiad, 2021.
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IMO 2022

2026Problem sheet

63rd International Mathematical Olympiad. Oslo, 2022.
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IMO 2007

2026Problem sheet

48th International Mathematical Olympiad, 2007.
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IMO 2008

2026Problem sheet

49th International Mathematical Olympiad. Madrid, Spain, 10-22 July 2008.
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IMO 2009

2026Problem sheet

50th International Mathematical Olympiad. Bremen, Germany, 2009.
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IMO 2010

2026Problem sheet

51st International Mathematical Olympiad, 2010.
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IMO 2012

2026Problem sheet

53rd International Mathematical Olympiad. Mar del Plata, Argentina, 2012.
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IMO 2013

2026Problem sheet

54th International Mathematical Olympiad. Colombia, 2013.
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IMO 2015

2026Problem sheet

56th International Mathematical Olympiad. Thailand, 2015.
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IMO 2016

2026Problem sheet

57th International Mathematical Olympiad. Hong Kong, 2016.
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IMO 2018

2026Problem sheet

59th International Mathematical Olympiad. Cluj-Napoca, Romania, 2018.
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IMO 2019

2026Problem sheet

60th International Mathematical Olympiad, 2019.
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IMO 2020

2026Problem sheet

61st International Mathematical Olympiad, 2020.
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IMO 2023

2026Problem sheet

64th International Mathematical Olympiad. Chiba, Japan, 2023.
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IMO 2025

2026Problem sheet

66th International Mathematical Olympiad. Sunshine Coast, 2025.
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Sharpness Estimation of Combinatorial Generalization Ability Bounds for Threshold Decision Rules

2021Journal articleSh. Kh. Ishkina, K. V. Vorontsov

Automation and Remote Control

This article is devoted to the problem of calculating an exact upper bound for the functionals of the generalization ability of a family of one-dimensional threshold decision rules. An algorithm is investigated that solves the stated problem and is polynomial in the total number of samples used for training and validation and in the number of training samples. A theorem is proved for calculating an estimate for the functional of expected overfitting and an estimate for the error rate of the method for minimizing empirical risk on a validation set. The exact bounds calculated using the theorem are compared with the previously known quick-to-compute upper bounds so as to estimate the orders of overestimation of the bounds and to identify the bounds that could be used in real problems.
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