L1-Regularized Optimization Algorithm: Difference between revisions
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** [[L0-Regularized Optimization Algorithm]]. | ** [[L0-Regularized Optimization Algorithm]]. | ||
* <B>See:</B> [[L1-Norm Regularizer]], [[Regularization]]. | * <B>See:</B> [[L1-Norm Regularizer]], [[Regularization]]. | ||
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Latest revision as of 18:40, 17 September 2021
An L1-Regularized Optimization Algorithm is an regularized optimization algorithm that optimizes an L1-norm.
- AKA: ℓ1 Regularization.
- Context:
- It can be solved by an L1-Regularized Optimization System (that can solve an L1-regularized optimization task).
- Example(s):
- Counter-Example(s):
- See: L1-Norm Regularizer, Regularization.
References
2017
- https://towardsdatascience.com/l1-and-l2-regularization-methods-ce25e7fc831c
- QUOTE: ... A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. …
2007
- (Schmidt, Fung & Rosales, 2007) ⇒ Mark Schmidt, Glenn Fung, Romer Rosales. (2007). “Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches.” In: Proceedings of the 18th European conference on Machine Learning (ECML 2007). doi:10.1007/978-3-540-74958-5_28