Regularization method for machine learning
Regularization method(正则化方法)
Outline
Overview of Regularization
L0 regularization
L1 regularization
L2 regularization
Elastic Net regularization
L2,1 regularization
Model example
Reference
Overview of Regularization
Main goal:
1. Prevent over-fitting
2. Reduce prediction error
3. Improve generalization performance
Essence:
1. Constraints the parameters to be optimized
2. Minimize your error while regularizing your parameters
L0 regularization
L1 regularization
L2 regularization
L1 vs. L2
Elastic Net regularization
L2,1 regularization
Reference
1. Sparsity and Some Basics of L1 Regularization
2. A note on the group lasso and a sparse group lasso
3. Hierarchical Structured Sparse Representation
4. 正态分布的前世今生
5. https://www.zhihu.com/question/20924039
6. Sparse methods for machine learning
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