Regularization (mathematics)  From Wikipedia, the free
encyclopedia   (Redirected from Regularization (machine
learning)) Jump to: navigation, search For other uses in related
fields, see Regularization (disambiguation).  Regularization, in
mathematics and statistics and particularly in the fields of
machine learning and inverse problems, refers to a process of
introducing additional information in order to solve an ill-
posed problem or to prevent overfitting. This information is
usually of the form of a penalty for complexity, such as
restrictions for smoothness or bounds on the vector space norm.
A theoretical justification for regularization is that it
attempts to impose Occam's razor on the solution. From a
Bayesian point of view, many regularization techniques
correspond to imposing certain prior distributions on model
parameters.  The same idea arose in many fields of science. For
example, the least-squares method can be viewed as a very simple
form of regularization. A simple form of regularization ap