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Logo r-cran-lasso2 1.2-14

by r-cran-robot - November 20, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 6393 views, 1313 downloads, 1 subscription

About: L1 constrained estimation aka `lasso'

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.967868


Logo r-cran-ipred 0.9-1

by r-cran-robot - November 14, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 9743 views, 2308 downloads, 1 subscription

About: Improved Predictors

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:05.613011


Logo Divvy 1.1.1

by jlewis - November 14, 2012, 20:21:29 CET [ Project Homepage BibTeX Download ] 1550 views, 778 downloads, 1 subscription

About: Divvy is a Mac OS X application for performing dimensionality reduction, clustering, and visualization.

Changes:

Initial Announcement on mloss.org.


Logo Milk 0.5

by luispedro - November 7, 2012, 13:08:28 CET [ Project Homepage BibTeX Download ] 20547 views, 4939 downloads, 1 subscription

Rating Whole StarWhole StarWhole StarEmpty StarEmpty Star
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About: Python Machine Learning Toolkit

Changes:

Added LASSO (using coordinate descent optimization). Made SVM classification (learning and applying) much faster: 2.5x speedup on yeast UCI dataset.


Logo r-cran-randomForest 4.6-7

by r-cran-robot - October 16, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 11138 views, 2159 downloads, 1 subscription

About: Breiman and Cutler's random forests for classification and regression

Changes:

Fetched by r-cran-robot on 2013-04-01 00:00:07.638240


Logo Reranker Framework 1.0

by zenog - October 29, 2012, 10:05:30 CET [ Project Homepage BibTeX Download ] 1071 views, 354 downloads, 1 subscription

About: ReFr is a software architecture for specifying, training and using reranking models.

Changes:

Initial Announcement on mloss.org.


Logo UniverSVM 1.22

by fabee - October 16, 2012, 11:24:12 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 16078 views, 2525 downloads, 0 subscriptions

About: The UniverSVM is a SVM implementation written in C/C++. Its functionality comprises large scale transduction via CCCP optimization, sparse solutions via CCCP optimization and data-dependent [...]

Changes:

Minor changes: fix bug on set_alphas_b0 function (thanks to Ferdinand Kaiser - ferdinand.kaiser@tut.fi)


Logo MROGH 1.0

by openpr_nlpr - October 16, 2012, 04:41:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1669 views, 349 downloads, 1 subscription

About: An implementation of MROGH descriptor. For more information, please refer to: “Bin Fan, Fuchao Wu and Zhanyi Hu, Aggregating Gradient Distributions into Intensity Orders: A Novel Local Image Descriptor, CVPR 2011, pp.2377-2384.” The most up-to-date information can be found at : http://vision.ia.ac.cn/Students/bfan/index.htm

Changes:

Initial Announcement on mloss.org.


Logo TurboParser 2.0

by afm - October 11, 2012, 02:59:04 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3947 views, 875 downloads, 1 subscription

About: TurboParser is a free multilingual dependency parser based on linear programming developed by André Martins. It is based on joint work with Noah Smith, Mário Figueiredo, Eric Xing, Pedro Aguiar.

Changes:

This version introduces a number of new features:

  • The parser does not depend anymore on CPLEX (or any other non-free LP solver). Instead, the decoder is now based on AD3, our free library for approximate MAP inference.

  • The parser now outputs dependency labels along with the backbone structure.

  • As a bonus, we now provide a trainable part-of-speech tagger, called TurboTagger, which can be used in standalone mode, or to provide part-of-speech tags as input for the parser. TurboTagger has state-of-the-art accuracy for English (97.3% on section 23 of the Penn Treebank) and is fast (~40,000 tokens per second).

  • The parser is much faster than in previous versions. You may choose among a basic arc-factored parser (~4,300 tokens per second), a standard second-order model with consecutive sibling and grandparent features (the default; ~1,200 tokens per second), and a full model with head bigram and arbitrary sibling features (~900 tokens per second).

Note: The runtimes above are approximate, and based on experiments with a desktop machine with a Intel Core i7 CPU 3.4 GHz and 8GB RAM. To run this software, you need a standard C++ compiler. This software has the following external dependencies: AD3, a library for approximate MAP inference; Eigen, a template library for linear algebra; google-glog, a library for logging; gflags, a library for commandline flag processing. All these libraries are free software and are provided as tarballs in this package.

This software has been tested on Linux, but it should run in other platforms with minor adaptations.


Logo VLFeat 0.9.16

by andreavedaldi - October 5, 2012, 18:44:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6096 views, 1139 downloads, 1 subscription

About: The VLFeat open source library implements popular computer vision algorithms including affine covariant feature detectors, HOG, SIFT, MSER, k-means, hierarchical k-means, agglomerative information bottleneck, SLIC superpixels, and quick shift. It is written in C for efficiency and compatibility, with interfaces in MATLAB for ease of use, and detailed documentation throughout. It supports Windows, Mac OS X, and Linux. The latest version of VLFeat is 0.9.16.

Changes:

VLFeat 0.9.16: Added VL_COVDET() (covariant feature detectors). This function implements the following detectors: DoG, Hessian, Harris Laplace, Hessian Laplace, Multiscale Hessian, Multiscale Harris. It also implements affine adaptation, estiamtion of feature orientation, computation of descriptors on the affine patches (including raw patches), and sourcing of custom feature frame. Addet the auxiliary function VL_PLOTSS(). This is the second point update supported by the PASCAL Harvest programme.

VLFeat 0.9.15: Added VL_HOG() (HOG features). Added VL_SVMPEGASOS() and a vastly improved SVM implementation. Added IHASHSUM (hashed counting). Improved INTHIST (integral histogram). Added VL_CUMMAX(). Improved the implementation of VL_ROC() and VL_PR(). Added VL_DET() (Detection Error Trade-off (DET) curves). Improved the verbosity control to AIB. Added support for Xcode 4.3, improved support for past and future Xcode versions. Completed the migration of the old test code in toolbox/test, moving the functionality to the new unit tests toolbox/xtest. Improved credits. This is the first point update supported by the PASCAL Harvest (several more to come shortly).


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