Within the framework of statistical learning theory we analyze in detail the so-called elastic-net regularization scheme proposed by Zou and Hastie [H. Zou, T. Hastie, Regularization and variable selection via the elastic net, J. R. Stat. Soc. Ser. B, 67(2) (2005) 301–320] for the ...
Learning theoryElastic-net regularizationl(2)-empirical covering numberLearning rateIn this paper, within the framework of statistical learning theory we address the elastic-net regularization problem. Based on the capacity assumption of hypothesis space composed by infinite features, significant ...
In the WELM learning stage, elastic net regularization is used to update the output weights comprising the l1 norm and l2 norm to obtain more compact and meaningful features. Finally, a flexible weight update criterion is designed for the WELM. Overall, the main contributions of this paper are...
machine-learningrlogistic-regressionglmnetregularizationridge-regressionsocial-scienceslasso-regressionelastic-net-regression UpdatedJun 11, 2021 Jupyter Notebook My role in this group project was to perform regression analysis on quarterly financial data to predict a company's market capitalization. I used ...
Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution. Results Feature selection methods with combined penalties (Elastic Net and Elast...
Additionally, using the glmnet package[29], which offers robust implementation of the lasso and elastic net regularization paths, the mean cross-validated errors (cvm) for each evaluated value of min can be obtained based on various type measures such as AUC (Area Under the Curve) and ...
The λ and β in the formula represents the regularization parameter. Being able to find that the E-Net penalty adds a ridge regression penalty to the Lasso penalty, calculated as a weighted sum of the Lasso penalty and the ridge regression penalty. The parameters in the formula are responsibl...
In this tutorial, we will be discussing ridge regression, lasso regression, and elastic net regression, each of which is a form of “regularization.” In essence, regularization is just any technique where we add information to the model to reduce overfitting, or to otherwise introduce bias for...
The elastic net estimator minimizes EN(β)=∑i=1n(yi−xi⊤β)2+λ1∑j=1p|βj|+λ2∑j=1p|βj|2. Due to the ridge regularization, the elastic net estimator can handle correlations between the predictors better than LASSO and due to the L1 regularization, sparsity is obtained. ...
Influence lines (ILs) of the bridge have great potential in structural damage detection, model updating and bridge weigh-in-motion system. To address the prolonged traffic closure and low efficiency during the field tests, a regularization technique using Elastic Net and vehicle-induced response is ...