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Regularization (mathematics)

In mathematics, statistics, finance, and computer science, particularly in machine learning and inverse problems, regularization is a process that converts the answer to a problem to a simpler one. It is often used in solving ill-posed problems or to prevent overfitting. There is a strong connection between regularization methods and Bayesian approaches…

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Overview

Regularization in machine learning

Classification

Tikhonov regularization (ridge regression)

Early stopping

Regularizers for sparsity

Regularizers for semi-supervised learning

Regularizers for multitask learning

Other uses of regularization in statistics and machine learning

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Regularization (mathematics)

Nodes75
Edges74
Triples3
Avg. degree1.97
Density0.026667
Components1

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Important terminology

regularization displaystyle learning function left right model problem data one training sum regularizer methods problems norm overfitting frac used bayesian

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
gradient descent tends to learn moreinstance ofa training procedure0.80text
more complex functions with increasing iterationsinstance ofa training procedure0.80text
computational biologyinstance ofThis is useful in many real-life applications0.80text

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