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

被引：0
|
作者：
Vinogradov, D. V. [1 ]
机构：
[1] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Moscow 119333, Russia
来源：
PATTERN RECOGNITION AND IMAGE ANALYSIS | 2023年 / 33卷 / 03期
关键词：
Markov chain; inductive generalization; empirical risk; prediction by analogy; abductive explanation; SIMILARITY;
D O I：
10.1134/S1054661823030458
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：525 / 528
页数：4
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