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Logo libcmaes 0.9.0

by beniz - September 10, 2014, 10:13:53 CET [ Project Homepage BibTeX Download ] 1053 views, 223 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 ] 4089 views, 757 downloads, 3 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 ] 3858 views, 730 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 ] 27267 views, 5769 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 ] 2307 views, 399 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-CoxBoost 1.4

by r-cran-robot - September 1, 2014, 00:00:04 CET [ Project Homepage BibTeX Download ] 16478 views, 3344 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 2014-09-01 00:00:04.950391


Logo r-cran-Boruta 3.1.0

by r-cran-robot - September 1, 2014, 00:00:04 CET [ Project Homepage BibTeX Download ] 6571 views, 1412 downloads, 2 subscriptions

About: A wrapper algorithm for all-relevant feature selection

Changes:

Fetched by r-cran-robot on 2014-09-01 00:00:04.064276


Logo JMLR MLPACK 1.0.10

by rcurtin - August 29, 2014, 21:26:18 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 32142 views, 6401 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 python weka wrapper 0.1.10

by fracpete - August 29, 2014, 05:00:14 CET [ Project Homepage BibTeX Download ] 3607 views, 758 downloads, 2 subscriptions

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

Changes:
  • fixed adding custom classpath using jvm.start(class_path=[...])

Logo JMLR dlib ml 18.10

by davis685 - August 29, 2014, 02:56:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 80159 views, 13932 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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