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Elastic Net Regularization Paths for All Generalized Linear Models [PDF]
Journal of Statistical Software, 2023 The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient algorithm for computing the elastic net regularization path for ordinary least ...
J. Kenneth Tay +2 more
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A Robust Variable Selection Method for Sparse Online Regression via the Elastic Net Penalty
Mathematics, 2022 Variable selection has been a hot topic, with various popular methods including lasso, SCAD, and elastic net. These penalized regression algorithms remain sensitive to noisy data.
Wentao Wang +4 more
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Mathematics, 2021 The lasso and elastic net methods are the popular technique for parameter estimation and variable selection. Moreover, the adaptive lasso and elastic net methods use the adaptive weights on the penalty function based on the lasso and elastic net ...
Autcha Araveeporn
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On the adaptive elastic-net with a diverging number of parameters [PDF]
Annals of Statistics, 2009 We consider the problem of model selection and estimation in situations where the number of parameters diverges with the sample size. When the dimension is high, an ideal method should have the oracle property [J. Amer. Statist. Assoc.
Hui Zou, Hao Helen Zhang
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LASSO and Elastic Net Tend to Over-Select Features
Mathematics, 2023 Machine learning methods have been a standard approach to select features that are associated with an outcome and to build a prediction model when the number of candidate features is large. LASSO is one of the most popular approaches to this end.
Lu Liu +3 more
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Pathway-Based Genomics Prediction using Generalized Elastic Net. [PDF]
PLoS Computational Biology, 2016 We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection.
Artem Sokolov +4 more
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A Cluster Elastic Net for Multivariate Regression [PDF]
J. Mach. Learn. Res., 2018 We propose a method for estimating coefficients in multivariate regression when there is a clustering structure to the response variables. The proposed method includes a fusion penalty, to shrink the difference in fitted values from responses in the same
Price, Bradley S., Sherwood, Ben
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Compressed Sensing Photoacoustic Imaging Reconstruction Using Elastic Net Approach [PDF]
Molecular Imaging, 2022 Photoacoustic imaging involves reconstructing an estimation of the absorbed energy density distribution from measured ultrasound data. The reconstruction task based on incomplete and noisy experimental data is usually an ill-posed problem that requires ...
Xueyan Liu +3 more
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Elastic-net regularization in learning theory
Journal of Complexity, 2009 Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie for the selection of groups of correlated variables. To investigate on the statistical properties of this scheme and in particular on its consistency properties, we set up a suitable mathematical framework.
Ernesto De Vito, Lorenzo Rosasco
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Stability of the elastic net estimator
Journal of Complexity, 2016 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yi Shen, Elena Braverman
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