Machine Learning and Data Mining Group. LIBLINEAR -- A Library for Large Linear Classification. Machine Learning Group at National Taiwan University Contributors We recently released LibShortText, a library for short-text classification and analysis. It's built upon LIBLINEAR. Version 1.94 released on November 12, 2013. Following the recent change of LIBSVM, we slightly adjust the way class labels are handled internally. An experimental version using 64-bit int is in LIBSVM tools. We are interested in large sparse regression data. A practical guide to LIBLINEAR is now available in the end of LIBLINEAR paper. Some extensions of LIBLINEAR are at LIBSVM Tools. LIBLINEAR is the winner of ICML 2008 large-scale learning challenge (linear SVM track). Introduction LIBLINEAR is a linear classifier for data with millions of instances and features.
Main features of LIBLINEAR include FAQ is here When to use LIBLINEAR but not LIBSVM There are some large data for which with/without nonlinear mappings gives similar performances. Download LIBLINEAR The package includes the source code in C/C++. R. LIBSVM -- A Library for Support Vector Machines. LIBSVM -- A Library for Support Vector Machines Chih-Chung Chang and Chih-Jen Lin Version 3.20 released on November 15, 2014. It conducts some minor fixes. LIBSVM tools provides many extensions of LIBSVM. Please check it if you need some functions not supported in LIBSVM. We now have a nice page LIBSVM data sets providing problems in LIBSVM format.
A practical guide to SVM classification is available now! To see the importance of parameter selection, please see our guide for beginners. Using libsvm, our group is the winner of IJCNN 2001 Challenge (two of the three competitions), EUNITE world wide competition on electricity load prediction, NIPS 2003 feature selection challenge (third place), WCCI 2008 Causation and Prediction challenge (one of the two winners), and Active Learning Challenge 2010 (2nd place).
Introduction LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). SVM - Support Vector Machines. Torch3: The Dream Comes True.