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Logo BCPy2000 17374

by jez - July 8, 2010, 22:11:24 CET [ Project Homepage BibTeX Download ] 19665 views, 3633 downloads, 1 subscription

About: BCPy2000 provides a platform for rapid, flexible development of experimental Brain-Computer Interface systems based on the BCI2000.org project. From the developer's point of view, the implementation [...]

Changes:

Bugfixes and tuneups, and an expanded set of (some more-, some less-documented, optional tools)


Logo r-cran-ipred 0.9-1

by r-cran-robot - November 14, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 15518 views, 3621 downloads, 1 subscription

About: Improved Predictors

Changes:

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


Logo Maja Machine Learning Framework 1.0

by jhm - September 13, 2011, 15:13:56 CET [ Project Homepage BibTeX Download ] 17578 views, 3619 downloads, 1 subscription

About: The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents.

Changes:
  • Experiments can now be invoked from the command line
  • Experiments can now be "scripted"
  • MMLF Experimenter contains now basic module for statistical hypothesis testing
  • MMLF Explorer can now visualize the model that has been learned by an agent

About: This local and parallel computation toolbox is the Octave and Matlab implementation of several localized Gaussian process regression methods: the domain decomposition method (Park et al., 2011, DDM), partial independent conditional (Snelson and Ghahramani, 2007, PIC), localized probabilistic regression (Urtasun and Darrell, 2008, LPR), and bagging for Gaussian process regression (Chen and Ren, 2009, BGP). Most of the localized regression methods can be applied for general machine learning problems although DDM is only applicable for spatial datasets. In addition, the GPLP provides two parallel computation versions of the domain decomposition method. The easiness of being parallelized is one of the advantages of the localized regression, and the two parallel implementations will provide a good guidance about how to materialize this advantage as software.

Changes:

Initial Announcement on mloss.org.


Logo RapidMiner 4.0

by ingomierswa - November 16, 2007, 02:31:48 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 20567 views, 3546 downloads, 0 comments, 0 subscriptions

Rating Whole StarWhole StarWhole StarWhole StarWhole Star
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About: RapidMiner (formerly YALE) is one of the most widely used open-source data mining suites and software solutions due to its leading-edge technologies and its functional range. Applications of [...]

Changes:

Initial Announcement on mloss.org.


Logo PREA Personalized Recommendation Algorithms Toolkit 1.1

by srcw - September 1, 2012, 22:53:37 CET [ Project Homepage BibTeX Download ] 13842 views, 3524 downloads, 2 subscriptions

About: An open source Java software providing collaborative filtering algorithms.

Changes:

Initial Announcement on mloss.org.


Logo SimpleMKL 0.5

by arakotom - June 11, 2008, 00:56:47 CET [ Project Homepage BibTeX Download ] 13313 views, 3508 downloads, 5 subscriptions

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About: Matlab Multiple Kernel Learning toolbox. Features : MKL for SVM Classification, Regression and MultiClass. It needs SVM-KM Toolbox

Changes:

Initial Announcement on mloss.org.


Logo r-cran-tgp 2.4-3

by r-cran-robot - December 18, 2011, 00:00:00 CET [ Project Homepage BibTeX Download ] 17072 views, 3503 downloads, 1 subscription

About: Bayesian treed Gaussian process models

Changes:

Fetched by r-cran-robot on 2012-02-01 00:00:11.834310


Logo r-cran-randomForest 4.6-7

by r-cran-robot - October 16, 2012, 00:00:00 CET [ Project Homepage BibTeX Download ] 17493 views, 3502 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 hca 0.63

by wbuntine - April 26, 2016, 15:35:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 26244 views, 3478 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:

Corrected the new normalised Gamma model for topics so it works with multicore. Improvements to documentation. Added an asymptotic version of the generalised Stirling numbers so it longer fails when they run out of bounds on bigger data.


Showing Items 91-100 of 634 on page 10 of 64: First Previous 5 6 7 8 9 10 11 12 13 14 15 Next Last