bigSVM-package: Solution Paths for Sparse Linear Support Vector Machine with...
In YaohuiZeng/bigSVM: Solution Paths of Sparse Support Vector Machine with Lasso or Elastic-Net Regularization For Big Data in R
Description
Fast algorithm for fitting solution paths of sparse linear SVM with
lasso or elastic-net regularization to generate sparse solutions and extend
model fitting with big data that cannot be fully loaded into main memory.
Details
Package: bigSVM
Type: Package
Version:
1.0-1
Date: 2017-04-07
License: GPL-3
Very simple to
use. Accepts X,y data for binary classification where y
belongs to +1, -1, and produces the regularized solution path over a grid of
values for the regularization parameter lambda.
Author(s)
Yaohui Zeng and Congrui Yi
YaohuiZeng/bigSVM documentation built on May 10, 2019, 12:05 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description
Fast algorithm for fitting solution paths of sparse linear SVM with lasso or elastic-net regularization to generate sparse solutions and extend model fitting with big data that cannot be fully loaded into main memory.
Details
| Package: | bigSVM |
| Type: | Package |
| Version: | 1.0-1 |
| Date: | 2017-04-07 |
| License: | GPL-3 |
Very simple to
use. Accepts X,y data for binary classification where y
belongs to +1, -1, and produces the regularized solution path over a grid of
values for the regularization parameter lambda.
Author(s)
Yaohui Zeng and Congrui Yi
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.