  Machine learning models suffer from overfitting, which is caused by a lack of
labeled data. To tackle this problem, we proposed a framework of regularization
methods, called density-fixing, that can be used commonly for supervised and
semi-supervised learning. Our proposed regularization method improves the
generalization performance by forcing the model to approximate the class's
prior distribution or the frequency of occurrence. This regularization term is
naturally derived from the formula of maximum likelihood estimation and is
theoretically justified. We further provide the several theoretical analyses of
the proposed method including asymptotic behavior. Our experimental results on
multiple benchmark datasets are sufficient to support our argument, and we
suggest that this simple and effective regularization method is useful in
real-world machine learning problems.
