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About: Bayesian Model Averaging for linear models with a wide choice of (customizable) priors. Builtin priorss include coefficient priors (fixed, flexible and hyperg priors), 5 kinds of model priors, moreover model sampling by enumeration or various MCMC approaches. Changes:Initial Announcement on mloss.org.

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:

About: The glmie 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 glmie 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:added factorial mean field inference as a third algorithm complementing expectation propagation and variational Bayes generalised nonGaussian potentials so that affine instead of linear functions of the latent variables can be used

About: ALgebraic COmbinatorial COmpletion of MAtrices. A collection of algorithms to impute or denoise single entries in an incomplete rank one matrix, to determine for which entries this is possible with any algorithm, and to provide algorithmindependent error estimates. Includes demo scripts. Changes:Initial Announcement on mloss.org.

About: CARP: The Clustering Algorithms’ Referee Package Changes:Generalized overlap error and some bugs have been fixed

About: Q. Dong, Twodimensional relaxed representation, Neurocomputing, 121:248253, 2013, http://dx.doi.org/10.1016/j.neucom.2013.04.044 Changes:Initial Announcement on mloss.org.

About: Bob is a free signalprocessing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, in Switzerland. Changes:Bob 1.2.0 comes about 1 year after we released Bob 1.0.0. This new release comes with a big set of new features and lots of changes under the hood to make your experiments run even smoother. Some statistics: Diff URL: https://github.com/idiap/bob/compare/v1.1.4...HEAD Commits: 629 Files changed: 954 Contributors: 7 Here is a quick list of things you should pay attention for while integrating your satellite packages against Bob 1.2.x:
For a detailed list of changes and additions, please look at our Changelog page for this release and minor updates: https://github.com/idiap/bob/wiki/Changelogfrom1.1.4to1.2 https://github.com/idiap/bob/wiki/Changelogfrom1.2.0to1.2.1 https://github.com/idiap/bob/wiki/Changelogfrom1.2.1to1.2.2

About: DDN learns and visualize differential dependency networks from conditionspecific data. Changes:Initial Announcement on mloss.org.

About: Hivemall is a scalable machine learning library running on Hive/Hadoop, licensed under the LGPL 2.1. Changes:

About: FABIA is a biclustering algorithm that clusters rows and columns of a matrix simultaneously. Consequently, members of a row cluster are similar to each other on a subset of columns and, analogously, members of a column cluster are similar to each other on a subset of rows. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. Applications include detection of transcriptional modules in gene expression data and identification of haplotypes/>identity by descent< consisting of rare variants obtained by next generation sequencing. Changes:CHANGES IN VERSION 2.8.0NEW FEATURES
CHANGES IN VERSION 2.4.0
CHANGES IN VERSION 2.3.1NEW FEATURES
2.0.0:
1.4.0:
