Library for Large Linear Classification
LIBLINEAR is a library for learning linear classifiers for large scale
applications. It supports Support Vector Machines (SVM) with L2 and L1
loss, logistic regression, multi class classification and also Linear
Programming Machines (L1-regularized SVMs). Its computational complexity
scales linearly with the number of training examples making it one of
the fastest SVM solvers around. It also provides Python bindings.
This package contains the shared libraries.
liblinear for Debian
For more information (including formulations) about LIBLINEAR, you are strongly
advised to read upstream's README file in /usr/share/doc/liblinear1/README.
-- Christian Kastner <email@example.com> Fri, 09 Jul 2010 20:56:11 +0200
LIBLINEAR is a simple package for solving large-scale regularized
linear classification. It currently supports L2-regularized logistic
regression/L2-loss support vector classification/L1-loss support vector
classification, and L1-regularized L2-loss support vector classification/
logistic regression. This document explains the usage of LIBLINEAR.
To get started, please read the ``Quick Start'' se
liblinear (1.8+dfsg-1+b1) sid; urgency=low
* Binary-only non-maintainer upload for hurd-i386; no source changes.
* Rebuild without debian-ports packages
-- Debian GNU/Hurd Build Daemon <firstname.lastname@example.org> Mon, 14 May 2012 07:11:56 +0200
liblinear (1.8+dfsg-1) unstable; urgency=low
* New upstream release.
- Added Chen-Tse Tsai <ctse.tsa
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