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About: Probabilistic performance evaluation for multiclass classification using the posterior balanced accuracy Changes:Added bibtex information.

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: 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: 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:

About: An audio editing tool for singlechannel source separation. Changes:Stereo processing, bug fixes, UI updates.

About: The CAM RJava software provides a noval way to solve blind source separation problem. Changes:In this version, we fix the problem of not working under newest R version R3.0.
