Regularized Ordinal Regression and the ordinalNet R Package
Journal of Statistical Software, 2021 Regularization techniques such as the lasso (Tibshirani 1996) and elastic net (Zou and Hastie 2005) can be used to improve regression model coefficient estimation and prediction accuracy, as well as to perform variable selection.
Michael J. Wurm +2 more
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Prediction of internal egg quality characteristics and variable selection using regularization methods: ridge, LASSO and elastic net [PDF]
Archives Animal Breeding, 2018 This study was conducted to determine the inner quality characteristics of eggs using external egg quality characteristics. The variables were selected in order to obtain the simplest model using ridge, LASSO and elastic net regularization methods ...
M. N. Çiftsüren, S. Akkol
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Shock and Vibration, 2021 In order to solve the problems of large number of conditions at inherent frequencies and low prediction accuracy when using multiple multivariate linear regression methods for vibration response prediction alone, an elastic-net regularization method is ...
ZhenKai Cui +3 more
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Spectrally Sparse Nonparametric Regression via Elastic Net Regularized Smoothers
Journal of Computational and Graphical Statistics, 2020 Nonparametric regression frameworks, such as generalized additive models (GAMs) and smoothing spline analysis of variance (SSANOVA) models, extend the generalized linear model (GLM) by allowing for...
Nathaniel E. Helwig
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Extreme Learning Machine with Elastic Net Regularization
Intelligent Automation and Soft Computing, 2020 Lihua Guo
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Elastic-net Regularization Multi Kernel Learning Algorithm Based on AdaBoost [PDF]
Jisuanji gongcheng, 2019 In regularization multi kernel learning,the sparse kernel function weight leads to the loss of useful information and the degradation of generalization performance,while selecting all kernel functions through non-sparse models generates more redundant ...
REN Shengbing, XIE Ruliang
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A Generalized Linear Joint Trained Framework for Semi-Supervised Learning of Sparse Features
Mathematics, 2022 The elastic net is among the most widely used types of regularization algorithms, commonly associated with the problem of supervised generalized linear model estimation via penalized maximum likelihood.
Juan Carlos Laria +3 more
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Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery [PDF]
Neural Computation, 2016 Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic net regularization (Zou & Hastie, 2005 ). The kernel in KENReg is not required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens ( 2014 ) showed that KENReg has some nice properties
Yunlong Feng +3 more
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BOAREN: IMPROVING REGULARIZATION IN LINEAR REGRESSION WITH AN APPLICATION TO INDEX TRACKING
Applied Finance Letters, 2022 In this paper we introduce the Arbitrary Rectangle-range Elastic Net (AREN): an elastic net with coefficients restricted to some rectangle in , . The AREN method is one of many regularization techniques intended to increase prediction accuracy in linear ...
John Angus, Yujia Ding, Qidi Peng
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Image Classification Method Based on Non-negative Elastic Net Sparse Coding Algorithm [PDF]
Jisuanji gongcheng, 2017 In order to improve the image classification accuracy,this paper proposes a Non-negative Elastic Net Sparse Coding(NENSC)algorithm.This algorithm combines the advantages of non-negative sparse coding and elastic net algorithm.It introduces an l2norm ...
ZHANG Yong,ZHANG Yangyang,CHENG Hong,ZHANG Yanxia
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