LIBLINEAR Experiments

Machine Learning Group at National Taiwan University

This page provides the source codes for the papers related to LIBLINEAR.

Comparing Large-scale L1-regularized Linear Classifiers

Guo-Xun Yuan, Kai-Wei Chang, Cho-Jui Hsieh, and Chih-Jen Lin. A Comparison of Optimization Methods for Large-scale L1-regularized Linear Classification

can be found in this zip file.

You can directly use LIBLINEAR for efficient L1-regularized classification. Use code here only if you are interested in redoing our experiments. The running time is long because we run each solver to accurately solve optimization problems.

Experiments on Degree-2 Polynomial Mappings of Data

Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Michael Ringgaard and Chih-Jen Lin. Low-Degree Polynomial Mapping of Data for SVM .

can be found in this zip file.

Use files here only if you are interested in redoing our experiments. To apply the method for your applications, all you need is a LIBLINEAR extension. Check "fast training/testing of degree-2 polynomial mappings of data" at LIBSVM Tools.

Experiments on Maximum Entropy models

Fang-Lan Huang, Cho-Jui Hsieh, Kai-Wei Chang, and Chih-Jen Lin. Iterative Scaling and Coordinate Descent Methods for Maximum Entropy Models .

can be found in this zip file.

Comparing various methods for large-scale linear SVM

C.-J. Hsieh, K.-W. Chang, C.-J. Lin, S. Sundararajan, and S. Sathiya Keerthi. A Dual Coordinate Descent Method for Large-scale Linear SVM .

can be found in this zip file.

Comparing various methods for large-scale linear SVM

K.-W. Chang, C.-J. Hsieh, and C.-J. Lin, Coordinate Descent Method for Large-scale L2-loss Linear SVM .

can be found in this zip file.

Comparing various methods for logistic regression

C.-J. Lin, R. C. Weng, and S. S. Keerthi. Trust region Newton method for large-scale logistic regression. JMLR 2008, To appear.

can be found in this zip file.

We include LBFGS and SVMlin (a modified version) for experiments. Please check their respective COPYRIGHT notices.