./math/liblinear, Library for large linear classification

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Branch: CURRENT, Version: 2.11, Package name: liblinear-2.11, Maintainer: cheusov

LIBLINEAR is a linear classifier for data with millions of instances
and features. It supports
L2-regularized classifiers
L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
L1-regularized classifiers (after version 1.4)
L2-loss linear SVM and logistic regression (LR)
L2-regularized support vector regression (after version 1.9)
L2-loss linear SVR and L1-loss linear SVR.
Main features of LIBLINEAR include
Same data format as LIBSVM, our general-purpose SVM solver,
and also similar usage
Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
Cross validation for model selection
Probability estimates (logistic regression only)
Weights for unbalanced data
MATLAB/Octave, Java, Python, Ruby interfaces


Required to build:
[pkgtools/cwrappers]

Master sites:

SHA1: 6c306d0d0b7ea5281ee2b587adc89745ee0d74fa
RMD160: c80d051e8b354de03da3e41bd1bea096ef4076f3
Filesize: 494.638 KB

Version history: (Expand)


CVS history: (Expand)


   2017-05-21 12:40:28 by Adam Ciarcinski | Files touched by this commit (4) | Package updated
Log message:
Changes 2.11:
We have improved the trust-region update rule in the primal-based Newton method. \ 
It's significantly faster (e.g., twice faster or more) on some problems (see the \ 
technical report).
We now support scipy objects in the Python interface
   2016-01-28 12:34:48 by Jonathan Perkin | Files touched by this commit (4) | Package updated
Log message:
Build blas.a using libtool, fixes build on SunOS.  Bump PKGREVISION.
   2015-11-20 15:47:20 by Adam Ciarcinski | Files touched by this commit (5)
Log message:
Changes 2.1:
Unknown
   2015-11-04 00:33:46 by Alistair G. Crooks | Files touched by this commit (262)
Log message:
Add SHA512 digests for distfiles for math category

Problems found locating distfiles:
	Package dfftpack: missing distfile dfftpack-20001209.tar.gz
	Package eispack: missing distfile eispack-20001130.tar.gz
	Package fftpack: missing distfile fftpack-20001130.tar.gz
	Package linpack: missing distfile linpack-20010510.tar.gz
	Package minpack: missing distfile minpack-20001130.tar.gz
	Package odepack: missing distfile odepack-20001130.tar.gz
	Package py-networkx: missing distfile networkx-1.10.tar.gz
	Package py-sympy: missing distfile sympy-0.7.6.1.tar.gz
	Package quadpack: missing distfile quadpack-20001130.tar.gz

Otherwise, existing SHA1 digests verified and found to be the same on
the machine holding the existing distfiles (morden).  All existing
SHA1 digests retained for now as an audit trail.
   2014-10-19 11:57:21 by Aleksey Cheusov | Files touched by this commit (4) | Imported package
Log message:
Add liblinear.
LIBLINEAR is a linear classifier for data with millions of instances
and features. It supports
    L2-regularized classifiers
    L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
    L1-regularized classifiers (after version 1.4)
    L2-loss linear SVM and logistic regression (LR)
    L2-regularized support vector regression (after version 1.9)
    L2-loss linear SVR and L1-loss linear SVR.
Main features of LIBLINEAR include
    Same data format as LIBSVM, our general-purpose SVM solver,
        and also similar usage
    Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
    Cross validation for model selection
    Probability estimates (logistic regression only)
    Weights for unbalanced data
    MATLAB/Octave, Java, Python, Ruby interfaces