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.

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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

Supplementary materials

Supplementary materials include the derivation not shown in the paper and more results of experiments.
Please contact Chih-Jen Lin for any question.