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About: machine learning library in java for easy development of new kernels and kernel algorithms Changes:Version 3.0 /! Warning: this version is incompatible with previous code
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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 0.3.2 fixes various bugs and adds GLC (Generalized Loop Corrections) written by Siamak Ravanbakhsh.
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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. Apart from a command line interface, Python, Julia, R, and MATLAB are supported. Changes:
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About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. Changes:2016-06-09 Version 4.7 Development and release branches available at https://github.com/gpstuff-dev/gpstuff New features
Improvements
Bugfixes
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About: MultiBoost is a multi-purpose boosting package implemented in C++. It is based on the multi-class/multi-task AdaBoost.MH algorithm [Schapire-Singer, 1999]. Basic base learners (stumps, trees, products, Haar filters for image processing) can be easily complemented by new data representations and the corresponding base learners, without interfering with the main boosting engine. Changes:Major changes :
Minor fixes:
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About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for non-Gaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISS-GP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models. Changes:Logdet-estimation functionality for grid-based approximate covariances
More generic infEP functionality
New infKL function contributed by Emtiyaz Khan and Wu Lin
Time-series covariance functions on the positive real line
New covariance functions
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About: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly Changes:Fetched by r-cran-robot on 2018-01-01 00:00:07.696284
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About: The Advanced Data mining And Machine learning System (ADAMS) is a flexible workflow engine aimed at quickly building and maintaining data-driven, reactive workflows, easily integrated into business processes. Changes:Some highlights:
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About: A Laboratory for Recursive Partytioning Changes:Fetched by r-cran-robot on 2013-04-01 00:00:06.775432
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About: Script-friendly command-line tools for machine learning and data mining tasks. (The command-line tools wrap functionality from a public domain C++ class library.) Changes:Added support for CUDA GPU-parallelized neural network layers, and several other new features. Full list of changes at http://waffles.sourceforge.net/docs/changelog.html
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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 Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details. Changes:For a complete list of changes, please see the full release notes at the release details page at: https://github.com/accord-net/framework/releases/tag/v3.8.0
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About: Wrapper Algorithm for All Relevant Feature Selection Changes:Fetched by r-cran-robot on 2018-09-01 00:00:04.516878
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About: FEAST provides implementations of common mutual information based filter feature selection algorithms (mim, mifs, mrmr, cmim, icap, jmi, disr, fcbf, etc), and an implementation of RELIEF. Written for C/C++ & Matlab. Changes:Major refactoring of FEAST to improve speed and portability.
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About: Python Machine Learning Toolkit Changes:Added LASSO (using coordinate descent optimization). Made SVM classification (learning and applying) much faster: 2.5x speedup on yeast UCI dataset.
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About: Tools to convert datasets from various formats to various formats, performance measures and API functions to communicate with mldata.org Changes:
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About: Kernel-Based Analysis of Biological Sequences Changes:
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About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications. Changes:
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About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python. Changes:-Fixed bug in save/restore. -Fixed bug in initial design.
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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.
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