Automatic and Efficient Parameter Selection in LIBLINEAR

After version 2.0, LIBLINEAR supports a new function to select the regularization parameter by cross validation. It automatically generates a sequence of regularization parameters and outputs the best one according to the cross-validation accuracy. The warm-start technique is applied to speed up the parameter-selection procedure. Currently only primal classification solvers are supported (-s 0 or -s 2). Technical details are in the following paper.

B.-Y. Chu, C.-H. Ho, C.-H. Tsai, C.-Y. Lin, and C.-J. Lin. Warm Start for Parameter Selection of Linear Classifiers. ACM KDD 2015 (supplementary materials including the derivation not shown in the paper and more results of experiments).

If you successfully used this code for your applications, please let us know. We are interested in how it's being used.

How to Run Parameter Selection in LIBLINEAR

It is very simple by using the new option -C. Examples:

Find parameters by five-fold cross validation and logistic regression.

> train -s 0 -C -v 5 heart_scale
Find parameters by ten-fold cross validation and L2-loss SVM.
> train -s 2 -C -v 10 heart_scale

Please contact Chih-Jen Lin for any question.