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  • 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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  • 原文地址:https://www.cnblogs.com/AcceptedLin/p/9778983.html
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