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Everything about Elastic net regularization
Statistical regression method
In statistics and, in particular, in the fitting of linear or logistic regression models, the elastic net is a regularized regression method that linearly combines the L1 and L2 penalties of the lasso and ridge methods. Nevertheless, elastic net regularization is typically more accurate than both methods with regard to reconstruction. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Elastic net regularizationA 5-minute orientation
- Skim a recent paper: Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithmsInfluential work since 2022
- Pick up a classic book: BibleThe most-reprinted book on the subject
- Read the landmark paper: Regularization and Variable Selection Via the Elastic NetCited 17,874 times
- Go sideways: Lasso (statistics)A closely related topic
- Go sideways: Regularized least squaresA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Regularization and Variable Selection Via the Elastic Net
Elastic Net Regularization Paths for All Generalized Linear Models
Addendum: Regularization and Variable Selection Via the Elastic Net
Elastic-net regularization in learning theory
Elastic-net regularization approaches for genome-wide association studies of rheumatoid arthritis
Elastic-net regularization: error estimates and active set methods
Error Analysis for Matrix Elastic-Net Regularization Algorithms
Type-1 fuzzy forecasting functions with elastic net regularization
🆕 Influential since 2022
Regularization in machine learning models for MVT Pb-Zn prospectivity mapping: applying lasso and elastic-net algorithms
Elastic net regularization and gabor dictionary for classification of heart sound signals using deep learning
kppmenet: combining the kppm and elastic net regularization for inhomogeneous Cox point process with correlated covariates
Development of a processing factor prediction model for pesticides in processed tomato foods using elastic net regularization
International analysis on social and personal determinants of traffic violations and accidents employing logistic regression with elastic net regularization
📚 Books worth reading
BibleBible · 1200
Intelligence BulletinUnited States. War Department. Military Intelligence Service · 1942
Mastering machine learning with RCory Lesmeister · 2015
Machine Learning in MedicineTon J. M. Cleophas · 2013- 📕ICTE 2015China) International Conference On Transportation Engineering (5th 2015 Dalian Shi · 2015
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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- Regularized least squares Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting…
- High-dimensional statistics In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger (relative to the number of datapoints) than typically…
- Least squares In numerical linear algebra and regression analysis, least squares is a method to determine the best-fit model by minimizing the sum of the squared residuals—th…
- Least-squares support vector machine Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM)…
- Ridge regression Ridge regression (also known as Tikhonov regularization, named for Andrey Tikhonov) is a method of estimating the coefficients of multiple-regression models…
- Regularization (mathematics) In mathematics, statistics, finance, and computer science, particularly in machine learning and inverse problems, regularization is a process that converts…
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