# Regularization

Revision as of 12:28, 6 June 2011 by Zeno Gantner (talk | contribs) (Created page with "'''Regularization''' methods penalize high values for model parameters in order to avoid overfitting effects. For example the optimization (minimization) problem for a [[...")

**Regularization** methods penalize high values for model parameters in order to avoid overfitting effects.
For example the optimization (minimization) problem for a matrix factorization model used in a recommender system typically contains a **penalty term** for the elements of the lower-rank matrices used to approximate the user-item matrix, e.g. via the Frobenius norm.