# regularization (under construction)

## Summary

-No Summary-

## Notes

This concept is still under construction.

## Context

This concept has the prerequisites:

- ridge regression (Ridge regression is an instructive example of regularization.)
- LASSO (LASSO is an instructive example of regularization.)
- optimization problems (Regularized learning algorithms are usually formulated as optimization problems.)
- generalization (Regularization is a strategy for improving generalization performance.)

## See also

- Common regularizers include:
- L_1 regularization , which encourages sparsity
- L_2 regularization , which shrinks the coefficients towards zero
- group sparsity , where multiple coefficients are encouraged to be zero or nonzero together
- Tikhonov regularization , which penalizes the sensitivity of the model's outputs to noises in the inputs

- Regularization can equivalently be viewed as adding constraints to a model, a view taken in structural risk minimization