pkgsrc - [no description]

Age	Commit message (Collapse)	Author	Files	Lines
2020-10-28	liblinear: updated to 2.41	adam	3	-40/+34
	Version 2.41 released on July 29, 2020 (some bug fixes of version 2.40). Version 2.40 released on July 22, 2020. A new solver: dual coordinate descent method for linear one-class SVM; see the paper The Newton solver is updated to have faster training speed; see the release note A new option -R to allow users not to regularize bias (when -B 1 is used)
2017-11-15	liblinear: follow redirects	wiz	1	-3/+3

2017-11-10	Add support for CFLAGS and LDFLAGS	khorben	4	-12/+23
	This notably fixes building with RELRO enabled. Bump PKGREVISION, since this generates a different binary now that SSP and FORTIFY are enabled.
2017-05-21	Changes 2.11:	adam	4	-25/+19
	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	Build blas.a using libtool, fixes build on SunOS. Bump PKGREVISION.	jperkin	4	-18/+58

2015-11-20	Changes 2.1:	adam	5	-13/+75
	Unknown
2015-11-03	Add SHA512 digests for distfiles for math category	agc	1	-1/+2
	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	Add liblinear.	cheusov	4	-0/+57
	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