Author: chrisk-guest Date: Wed Jul 21 20:41:14 2010 New Revision: 2282 URL: http://svn.debian.org/viewsvn/blends?rev=2282&view=rev
Log: Remove WNPP status from packages that have entered the archive Modified: projects/science/trunk/debian-science/tasks/machine-learning Modified: projects/science/trunk/debian-science/tasks/machine-learning URL: http://svn.debian.org/viewsvn/blends/projects/science/trunk/debian-science/tasks/machine-learning?rev=2282&view=diff&r1=2282&r2=2281&p1=projects/science/trunk/debian-science/tasks/machine-learning&p2=projects/science/trunk/debian-science/tasks/machine-learning ============================================================================== --- projects/science/trunk/debian-science/tasks/machine-learning (original) +++ projects/science/trunk/debian-science/tasks/machine-learning Wed Jul 21 20:41:14 2010 @@ -10,7 +10,8 @@ Depends: libtorch3-dev -Depends: libshogun-dev, libfann-dev, libsvm-dev, libcomplearn-dev, libqsearch-dev +Depends: libshogun-dev, libfann-dev, libsvm-dev, libcomplearn-dev, libqsearch-dev, + liblinear-dev, libocas-dev Comment: above libraries have also variety of interfaces to high-level scripting languages (e.g. Python) and even possibly some interactive GUI @@ -60,83 +61,10 @@ Depends: scilab-ann -Depends: libga-dev, libevocosm-dev, pgapack, python-genetic, octave-ga +Depends: libga-dev, libevocosm-dev, pgapack, python-genetic, octave-ga, + python-pyevolve Comment: Evolutionary algorithm libraries in various languages -Depends: liblinear-dev -Homepage: http://www.csie.ntu.edu.tw/~cjlin/liblinear/ -Language: C/C++ -WNPP: 585788 -License: BSD -Pkg-Description: Library for Large Linear Classification - 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) - . - Main features of LIBLINEAR include - . - * Same data format as LIBSVM - * similar usage to LIBSVM - * 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 interface - -Depends: libocas-dev -Homepage: http://cmp.felk.cvut.cz/~xfrancv/ocas/html/ -Language: C -WNPP: 585789 -License: GPL-3 -Pkg-Description: OCAS solver for training linear SVM classifiers - This library implements Optimized Cutting Plane Algorithm (OCAS) for training - linear SVM classifiers from large-scale data. The computational effort of OCAS - scales with O(m log m) where m is the sample size. In an extensive empirical - evaluation OCAS significantly outperforms current state of the art SVM solvers, - like SVM^light, SVM^perf and BMRM, achieving speedups of over 1,000 on some - datasets over SVM^light and 20 over SVM^perf, while obtaining the same precise - Support Vector solution. - . - * SVM solvers for training linear classifiers from large scale-data - * Binary (two-class) and genuine multi-class SVM formulations - * Optimized code written in C - * Reads examples from SVM^light format - * Optimized for both sparse and dense features - * Parallelized version of the binary solver - * binary solver) - * Tools for classification - * Training translation invariant image classifiers from virtual examples - * Functions for computing image features based on Local Binary Patterns - * (LBP) - -Depends: python-pyevolve -Homepage: http://pyevolve.sourceforge.net -Language: Python -WNPP: 580924 -License: PSF derivate -Pkg-Description: Complete genetic algorithm framework written in pure python - Pyevolve was developed to be a complete genetic algorithm framework written in - pure python. The main objectives of Pyevolve are: - . - * written in pure python - to maximize the cross-platform aspect - * easy to use API - the API must be easy to the end-user - * see the evolution - the user can and must see and interact with the - evolution statistics, graphs, etc. - * extensible - the API must be extensible, the user can create - new representations, genetic operators such as - crossover, mutation, etc. - * fast - the design must be optimized for performance - * common features - the framework must implement the most common - features: selectors like roulette wheel, - tournament, ranking, uniform. Scaling schemes - such as linear scaling, etc. - * default parameters - we must have default operators, settings, etc - in all options - Depends: flann Homepage: http://www.cs.ubc.ca/~mariusm/index.php/FLANN/FLANN Language: C++ _______________________________________________ Blends-commit mailing list [email protected] http://lists.alioth.debian.org/mailman/listinfo/blends-commit
