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About: This project is a C++ toolkit containing machine learning algorithms and tools that facilitate creating complex software in C++ to solve real world problems. Changes:In addition to some bug fixes, this release also brings the following notable improvements to the library:
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About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models. Changes:We now support inference on large datasets using the FITC approximation for non-Gaussian likelihoods for EP and Laplace's approximation. New likelihood functions: mixture likelihood, Poisson likelihood, label noise. We added two MCMC samplers.
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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields. Changes:Release 0.3.1 fixes various bugs. The issues on 64-bit Windows platforms have been fixed and libDAI now offers full 64-bit support on all supported platforms (Linux, Mac OSX, Windows).
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About: Gaussian process RTS smoothing (forward-backward smoothing) based on moment matching. Changes:Initial Announcement on mloss.org.
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About: The glm-ie toolbox contains scalable estimation routines for GLMs (generalised linear models) and SLMs (sparse linear models) as well as an implementation of a scalable convex variational Bayesian inference relaxation. We designed the glm-ie package to be simple, generic and easily expansible. Most of the code is written in Matlab including some MEX files. The code is fully compatible to both Matlab 7.x and GNU Octave 3.2.x. Probabilistic classification, sparse linear modelling and logistic regression are covered in a common algorithmical framework allowing for both MAP estimation and approximate Bayesian inference. Changes:contributed by George Papandreou:
gfortran support to pls/lbfgsb/Makefile (thanks to Ernst Kloppenburg) slight modification to mat/@matFFTN/mvm.m to make it more consistent simple gradient solver using Barzilai-Borwein step size pls/plsBB.m
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About: Bayesian Reasoning and Machine Learning toolbox Changes:Fixed some small bugs and updated some demos.
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About: The Libra machine learning toolkit includes implementations of a variety of algorithms for learning and inference with Bayesian networks, Markov networks, dependency networks, and arithmetic circuits. Libra's strength is exploiting context-specific independence to allow exact inference in models with high treewidth. Changes:Version 0.4.0 (7/06/2011): * MF inference in DNs (mf -depnet) * Max-product algorithm for BNs and MNs (maxprod) * MPE inference in ACs (acquery -mpe) * Added support for UAI MN file format. * New fstats utility to get basic file statistics for most file types supported by Libra * And more!
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About: The library is focused on implementation of propagation based approximate inference methods. Also implemented are a clique tree based exact inference, Gibbs sampling, and the mean field algorithm. Changes:Initial Announcement on mloss.org.
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About: stroll (STRuctured Output Learning Library) is a library for Structured Output Learning. Changes:Initial Announcement on mloss.org.
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