A generalized coordinate descent (GCD) algorithm for computing
the solution path of the hybrid Huberized support vector
machine (HHSVM) and its generalization.
This package implements a generalized coordinate descent
(GCD) algorithm for computing the solution path of the hybrid
Huberized support vector machine (HHSVM) and its
generalization, including the elastic net penalized SVM with
the squared hinge loss and the elastic net penalized logistic
regression.

This package implements a generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the elastic net penalized least squares, the elastic net penalized SVM with the squared hinge loss and the elastic net penalized logistic regression.

LASSO and (adaptive) Elastic-Net penalized least squares,
logistic regression, HHSVM and squared hinge loss SVM using a
fast GCD algorithm

LASSO and Elastic Net (Adaptive) Penalized Least Squares,
Logistic Regression, HHSVM, Squared Hinge SVM and Expectile
Regression using a Fast GCD Algorithm
A generalized coordinate descent (GCD) algorithm for computing the solution path of the hybrid Huberized support vector machine (HHSVM) and its generalization, including the LASSO and elastic net (adaptive) penalized least squares, logistic regression, HHSVM, squared hinge loss SVM and expectile regression.

The (Adaptive) LASSO and Elastic Net Penalized Least Squares,
Logistic Regression, Hybrid Huberized Support Vector Machines,
Squared Hinge Loss Support Vector Machines and Expectile
Regression using a Fast Generalized Coordinate Descent
Algorithm
Yi Yang <yi.yang6@mcgill.ca>, Yuwen Gu <yuwen.gu@uconn.edu>, Hui Zou
<hzou@stat.umn.edu>
Implements a generalized coordinate descent (GCD) algorithm
for computing the solution paths of the hybrid Huberized support vector
machine (HHSVM) and its generalizations. Supported models include the
(adaptive) LASSO and elastic net penalized least squares, logistic
regression, HHSVM, squared hinge loss SVM and expectile regression.