(mathematics) A method of regularizing divergent integrals by dropping some divergent terms and keeping the finite part.

(mathematics) A particular method of regularization of ill-posed problems, or estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated.

In statistics and, in particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods.

In mathematics and theoretical physics, zeta function regularization is a type of regularization or summability method that assigns finite values to divergent sums or products, and in particular can be used to define determinants and traces of some self-adjoint operators.

In machine learning, Manifold regularization is a technique for using the shape of a dataset to constrain the functions that should be learned on that dataset.