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Logo r-cran-Boruta 3.1.0

by r-cran-robot - October 1, 2014, 00:00:04 CET [ Project Homepage BibTeX Download ] 7157 views, 1524 downloads, 2 subscriptions

About: A wrapper algorithm for all-relevant feature selection

Changes:

Fetched by r-cran-robot on 2014-10-01 00:00:04.028245


Logo BayesPy 0.2.1

by jluttine - September 30, 2014, 16:35:11 CET [ Project Homepage BibTeX Download ] 1825 views, 536 downloads, 3 subscriptions

About: Variational Bayesian inference tools for Python

Changes:
  • Add workaround for matplotlib 1.4.0 bug related to interactive mode which affected monitoring

  • Fix bugs in Hinton diagrams for Gaussian variables


Logo Armadillo library 4.450

by cu24gjf - September 21, 2014, 06:47:34 CET [ Project Homepage BibTeX Download ] 44815 views, 9719 downloads, 3 subscriptions

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About: Armadillo is a template C++ linear algebra library aiming towards a good balance between speed and ease of use, with a function syntax similar to MATLAB. Matrix decompositions are provided through optional integration with LAPACK, or one of its high performance drop-in replacements (eg. Intel MKL, OpenBLAS).

Changes:
  • faster handling of matrix transposes within compound expressions
  • expanded symmatu()/symmatl() to optionally disable taking the complex conjugate of elements
  • expanded sort_index() to handle complex vectors
  • expanded the gaussian mixture modelling class with functions to generate random samples

Logo hca 0.61

by wbuntine - September 10, 2014, 03:33:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4822 views, 952 downloads, 4 subscriptions

About: Multi-core non-parametric and bursty topic models (HDP-LDA, DCMLDA, and other variants of LDA) implemented in C using efficient Gibbs sampling, with hyperparameter sampling and other flexible controls.

Changes:

Corrections to diagnostics and topic report. Correction to estimating alpha. Now estimating beta sometimes (when estimating phi).


Logo Somoclu 1.4

by peterwittek - September 5, 2014, 13:01:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4507 views, 851 downloads, 2 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes.

Changes:
  • Better Windows support.
  • Completed CUDA support for Python and R interfaces.
  • Faster compilation by removing unnecessary flags for nvcc
  • Support for CUDA 6.5.
  • Bug fixes: R version no longer needs separate code.

Logo JMLR Darwin 1.8

by sgould - September 3, 2014, 08:42:53 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 28891 views, 6047 downloads, 4 subscriptions

About: A platform-independent C++ framework for machine learning, graphical models, and computer vision research and development.

Changes:

Version 1.8:

  • Added Superpixel Graph Label Transfer (nnGraph) Project project
  • Added Python scripts for automating some projects
  • Added ability to pre-process features on-the-fly with one drwnFeatureTransform when applying or learning another drwnFeatureTransform
  • Fixed race condition in Windows threading (thanks to Edison Guo)
  • Switched Windows and Linux to build against OpenCV 2.4.9
  • Changed drwnMAPInference::inference to return upper and lower energy bounds
  • Added pruneRounds function to drwnBoostedClassifier
  • Added drwnSLICSuperpixels function
  • Added drwnIndexQueue class
  • mexLearnClassifier and mexAnalyseClassifier now support integer label types
  • Bug fix in mexSaveSuperpixels to support single channel

Logo XGBoost v0.3.0

by crowwork - September 2, 2014, 02:43:31 CET [ Project Homepage BibTeX Download ] 2710 views, 490 downloads, 2 subscriptions

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily.

Changes:

New features: - R support that is now on CRAN

  • Faster tree construction module

  • Support for boosting from initial predictions

  • Linear booster is now parallelized, using parallel coordinated descent.


Logo r-cran-e1071 1.6-4

by r-cran-robot - September 1, 2014, 00:00:00 CET [ Project Homepage BibTeX Download ] 13805 views, 2895 downloads, 1 subscription

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About: Misc Functions of the Department of Statistics (e1071), TU Wien

Changes:

Fetched by r-cran-robot on 2014-10-01 00:00:04.570720


Logo JMLR MLPACK 1.0.10

by rcurtin - August 29, 2014, 21:26:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 33437 views, 6645 downloads, 6 subscriptions

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About: A scalable, fast C++ machine learning library, with emphasis on usability.

Changes:
  • Bugfix for NeighborSearch regression which caused very slow allknn/allkfn. Speeds are nwo restored to approximately 1.0.8 speeds, with significant improvement for the cover tree (#365).
  • Detect dependencies correctly when ARMA_USE_WRAPPER is not defined (i.e. libarmadillo.so does not exist).
  • Bugfix for compilation under Visual Studio (#366).

Logo JMLR dlib ml 18.10

by davis685 - August 29, 2014, 02:56:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 83042 views, 14358 downloads, 2 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

Changes:

In addition to a number of usability improvements, this release adds an implementation of the recent paper "One Millisecond Face Alignment with an Ensemble of Regression Trees" by Vahid Kazemi and Josephine Sullivan. This includes tools for performing high quality face landmarking as well as tools for training new landmarking models. See the face_landmark_detection_ex.cpp and train_shape_predictor_ex.cpp example programs for an introduction.


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