Estimation of Overfitting Degree of Algebraic Machine Learning in Boolean Algebra

Abstract

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.