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Logo r-cran-CoxBoost 1.4

by r-cran-robot - October 1, 2014, 00:00:04 CET [ Project Homepage BibTeX Download ] 16650 views, 3364 downloads, 3 subscriptions

About: Cox models by likelihood based boosting for a single survival endpoint or competing risks

Changes:

Fetched by r-cran-robot on 2014-10-01 00:00:04.396778


Logo r-cran-Boruta 3.1.0

by r-cran-robot - October 1, 2014, 00:00:04 CET [ Project Homepage BibTeX Download ] 6753 views, 1442 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 ] 1409 views, 406 downloads, 2 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 python weka wrapper 0.1.11

by fracpete - September 25, 2014, 00:39:02 CET [ Project Homepage BibTeX Download ] 4151 views, 850 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • moved wekaexamples module to separate github project: https://github.com/fracpete/python-weka-wrapper-examples
  • added "stratify", "train_cv" and "test_cv" methods to the Instances class
  • fixed "to_summary" method of the Evaluation class: failed when providing a custom title

Logo Armadillo library 4.450

by cu24gjf - September 21, 2014, 06:47:34 CET [ Project Homepage BibTeX Download ] 43175 views, 9438 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 libcmaes 0.9.0

by beniz - September 10, 2014, 10:13:53 CET [ Project Homepage BibTeX Download ] 1205 views, 249 downloads, 3 subscriptions

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly non-linear and non-convex functions, using the CMA-ES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability.

Changes:
  • Python bindings, ref #26
  • Cleaned up setters / getters interface, ref #64
  • Lib is now quiet by default, ref #61
  • Support for pkg-config, ref #58
  • Improved make uninstall, ref #66
  • API improvements (e.g. new parameters constructor from vector, ref #60)
  • Stopping criteria with explicit control of in-memory history size for large-scale optimization

Logo hca 0.61

by wbuntine - September 10, 2014, 03:33:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 4357 views, 805 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 ] 4053 views, 767 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 ] 27721 views, 5846 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 ] 2458 views, 434 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.


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