Glmnet in Matlab: Lasso and elastic-net regularized generalized linear models. This is a Matlab port for the efficient procedures for fitting the entire lasso or elastic-net path for linear regression, logistic and multinomial regression, Poisson regression and the Cox model. Features include: high efficiency by using coordinate descent with warm starts and active set iterations; methods for prediction, plotting and k-fold cross-validation; extensive options such as sparse input-matrix formats and range constraints on coefficients. Two recent additions are the multiresponse gaussian, and the grouped multinomial.
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References in zbMATH (referenced in 7 articles )
Showing results 1 to 7 of 7.
- Kim, Sun Hye; Boukouvala, Fani: Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (2020)
- Karl Sjöstrand; Line Clemmensen; Rasmus Larsen; Gudmundur Einarsson; Bjarne Ersbøll: SpaSM: A MATLAB Toolbox for Sparse Statistical Modeling (2018) not zbMATH
- Viola, Marco; Sangiovanni, Mara; Toraldo, Gerardo; Guarracino, Mario R.: A generalized eigenvalues classifier with embedded feature selection (2017)
- Michoel, Tom: Natural coordinate descent algorithm for (\ell_1)-penalised regression in generalised linear models (2016)
- Wehbe, Leila; Ramdas, Aaditya; Steorts, Rebecca C.; Shalizi, Cosma Rohilla: Regularized brain reading with shrinkage and smoothing (2015)
- Srivastava, Ashok N.: Greener aviation with virtual sensors: a case study (2012) ioport
- Jerome Friedman; Trevor Hastie; Rob Tibshirani: Regularization Paths for Generalized Linear Models via Coordinate Descent (2010) not zbMATH