"정칙화"의 두 판 사이의 차이

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* In intuitive terms, we can think of regularization as a penalty against complexity.<ref name="ref_1df2" />
 
* In intuitive terms, we can think of regularization as a penalty against complexity.<ref name="ref_1df2" />
 
* To solve this problem, we try to reach the sweet spot using the concept of regularization.<ref name="ref_7ce8">[https://heartbeat.fritz.ai/deep-learning-best-practices-regularization-techniques-for-better-performance-of-neural-network-94f978a4e518 Deep Learning Best Practices: Regularization Techniques for Better Neural Network Performance]</ref>
 
* To solve this problem, we try to reach the sweet spot using the concept of regularization.<ref name="ref_7ce8">[https://heartbeat.fritz.ai/deep-learning-best-practices-regularization-techniques-for-better-performance-of-neural-network-94f978a4e518 Deep Learning Best Practices: Regularization Techniques for Better Neural Network Performance]</ref>
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* The calculations suggest that the kinetic undercooling regularization avoids the cusp formation that occurs in the examples above.<ref name="ref_8410">[https://dictionary.cambridge.org/dictionary/english/regularization meaning in the Cambridge English Dictionary]</ref>
 
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* We perform the usual regularization of the graph using the additive doubling constant.<ref name="ref_8410" />
 
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* It indicates the parabolic regularization property of (1.8) and might be useful for other purposes.<ref name="ref_8410" />
 
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* In this work, we use such a modification as a regularization.<ref name="ref_8410" />
 
 
* For each regularization scheme, the regularization parameter(s) are tuned to maximize regression accuracy.<ref name="ref_e675">[https://www.frontiersin.org/articles/10.3389/fnins.2018.00531/full A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding]</ref>
 
* For each regularization scheme, the regularization parameter(s) are tuned to maximize regression accuracy.<ref name="ref_e675">[https://www.frontiersin.org/articles/10.3389/fnins.2018.00531/full A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding]</ref>
 
* Elastic net regularization permits the adjustment of the balance between L1 and L2 regularization via the α parameter.<ref name="ref_e675" />
 
* Elastic net regularization permits the adjustment of the balance between L1 and L2 regularization via the α parameter.<ref name="ref_e675" />
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===소스===
 
===소스===
 
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==메타데이터==
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===위키데이터===
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* ID :  [https://www.wikidata.org/wiki/Q2061913 Q2061913]
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===Spacy 패턴 목록===
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* [{'LEMMA': 'regularization'}]

2021년 2월 17일 (수) 01:50 기준 최신판

노트

  • Regularization penalties are applied on a per-layer basis.[1]
  • Regularization or Lasso Regularization adds a penalty to the error function.[2]
  • Regularization also works to reduce the impact of higher-order polynomials in the model.[3]
  • Instead of choosing parameters from a discrete grid, regularization chooses values from a continuum, thereby lending a smoothing effect.[3]
  • Two of the commonly used techniques are L1 or Lasso regularization and L2 or Ridge regularization.[3]
  • Regularization does NOT improve the performance on the data set that the algorithm used to learn the model parameters (feature weights).[4]
  • In intuitive terms, we can think of regularization as a penalty against complexity.[4]
  • To solve this problem, we try to reach the sweet spot using the concept of regularization.[5]
  • For each regularization scheme, the regularization parameter(s) are tuned to maximize regression accuracy.[6]
  • Elastic net regularization permits the adjustment of the balance between L1 and L2 regularization via the α parameter.[6]
  • This makes a direct comparison of the regularization methods difficult.[6]
  • Linking regularization and low-rank approximation for impulse response modeling.[6]
  • Regularization is related to feature selection in that it forces a model to use fewer predictors.[7]
  • Regularization operates over a continuous space while feature selection operates over a discrete space.[7]
  • The details of various regularization methods that are used depend very much on the particular context.[8]
  • Examples of regularizations in the sense of 1) or 2) above (or both) are: regularized sequences (cf.[8]
  • Regularization of sequences), regularized operators and regularized solutions (cf.[8]
  • We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures.[9]
  • Finally, we include practical recommendations both for users and for developers of new regularization methods.[9]
  • Regularization is a popular method to prevent models from overfitting.[10]
  • In this article, we will understand the concept of overfitting and how regularization helps in overcoming the same problem.[11]
  • How does Regularization help in reducing Overfitting?[11]
  • Regularization is a technique which makes slight modifications to the learning algorithm such that the model generalizes better.[11]
  • Here the value 0.01 is the value of regularization parameter, i.e., lambda, which we need to optimize further.[11]
  • Regularization plays a key role in many machine learning algorithms.[12]
  • But I'm more interested in getting a feeling for how you do regularization.[13]
  • The method of direct regularization of the Feynman integrals may be demonstrated by a calculation of the mass operator.[14]
  • The regularization of this integral involves subtractions such as to reduce it to the form (110.20).[14]
  • Would L 2 regularization accomplish this task?[15]
  • An alternative idea would be to try and create a regularization term that penalizes the count of non-zero coefficient values in a model.[15]
  • The effect of this regularization penality is to restrict the ability of the parameters of a model to freely take on large values.[16]
  • (ridge regression was originally introduced in order to ensure this invertibility, rather than as a form of regularization).[16]
  • Also, like the Jaccard methods, the weighted combination of these can be used for the regularization step in the Eeyore algorithm.[17]
  • Regularization, significantly reduces the variance of the model, without substantial increase in its bias.[18]
  • So the tuning parameter λ, used in the regularization techniques described above, controls the impact on bias and variance.[18]
  • Regularizations are techniques used to reduce the error by fitting a function appropriately on the given training set and avoid overfitting.[19]
  • Regularization is a technique used for tuning the function by adding an additional penalty term in the error function.[19]
  • Now how do these extra regularization term help us to keep a check on the co-efficient terms.[19]
  • I hope that now you can comprehend regularization in a better way.[19]
  • Regularization applies to objective functions in ill-posed optimization problems.[20]
  • This is called Tikhonov regularization, one of the most common forms of regularization.[20]
  • Early stopping can be viewed as regularization in time.[20]
  • Data Augmentation is one of the interesting regularization technique to resolve the above problem.[21]
  • A regression model that uses L1 regularization technique is called Lasso Regression.[21]

소스

  1. ↑ Layer weight regularizers
  2. ↑ What Is Regularization In Machine Learning?
  3. ↑ 3.0 3.1 3.2 Regularization In Machine Learning – A Detailed Guide
  4. ↑ 4.0 4.1 Does regularization in logistic regression always results in better fit and better generalization?
  5. ↑ Deep Learning Best Practices: Regularization Techniques for Better Neural Network Performance
  6. ↑ 6.0 6.1 6.2 6.3 A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding
  7. ↑ 7.0 7.1 Regularization
  8. ↑ 8.0 8.1 8.2 Encyclopedia of Mathematics
  9. ↑ 9.0 9.1 Regularization for Deep Learning: A Taxonomy
  10. ↑ A better visualization of L1 and L2 Regularization
  11. ↑ 11.0 11.1 11.2 11.3 Regularization In Deep Learning
  12. ↑ Regularization
  13. ↑ What is regularization in plain english?
  14. ↑ 14.0 14.1 Regularization - an overview
  15. ↑ 15.0 15.1 Regularization for Sparsity: L₁ Regularization
  16. ↑ 16.0 16.1 Regularization
  17. ↑ Regularization - an overview
  18. ↑ 18.0 18.1 Regularization in Machine Learning
  19. ↑ 19.0 19.1 19.2 19.3 REGULARIZATION: An important concept in Machine Learning
  20. ↑ 20.0 20.1 20.2 Regularization (mathematics)
  21. ↑ 21.0 21.1 Regularization — ML Glossary documentation

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Spacy 패턴 목록

  • [{'LEMMA': 'regularization'}]
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