布尔代数中代数机器学习的过拟合度估计

IF 0.5 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS Pub Date : 2022-08-15 DOI:10.3103/S0005105522030098

D. Vinogradov

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引用次数: 0

摘要

在没有反例的布尔代数最简单情况下，本文给出了代数机器学习的VKF方法的过拟合概率的估计。该模型使用Vapnik-Chervonenkis建议来最小化经验风险。如果描述长度和所要求的假设数量变为无穷大，则固定部分测试示例的过拟合误差的概率趋向于零，而不是指数下降。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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 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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来源期刊

AUTOMATIC DOCUMENTATION AND MATHEMATICAL LINGUISTICS COMPUTER SCIENCE, INFORMATION SYSTEMS-

自引率

40.00%

发文量

期刊介绍： Automatic Documentation and Mathematical Linguistics is an international peer reviewed journal that covers all aspects of automation of information processes and systems, as well as algorithms and methods for automatic language analysis. Emphasis is on the practical applications of new technologies and techniques for information analysis and processing.