About: PCVM library a c++/armadillo implementation of the Probabilistic Classification Vector Machine. Changes:30.10.2015 * code has been revised in some places fixing also some errors different multiclass schemes and hdf5 file support added. Some speed ups and memory savings by better handling of intermediate objects. 27.05.2015:  Matlab binding under Windows available. Added a solution file for VS'2013 express to compile a matlab mex binding. Can not yet confirm that under windows the code is really using multiple cores (under linux it does) 29.04.2015 * added an implementation of the Nystroem based PCVM includes: Nystroem based singular value decomposition (SVD), eigenvalue decomposition (EVD) and pseudoinverse calculation (PINV) 22.04.2015 * implementation of the PCVM released

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the wellknown stochastic algorithms for Machine Learning developed in the highlevel technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and nonlinear Support Vector Machines applied to large data samples with usercentric and userfriendly emphasis. Changes:Initial Announcement on mloss.org.

About: Learns dynamic network changes across conditions and visualize the results in Cytoscape. Changes:Initial Announcement on mloss.org.

About: A Content Anomaly Detector based on nGrams Changes:Lots and lots of cool new features and bugfixes ;)

About: DAL is an efficient and flexibible MATLAB toolbox for sparse/lowrank learning/reconstruction based on the dual augmented Lagrangian method. 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: 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: This package is a set of Matlab scripts that implements the algorithms described in the submitted paper: "LpLq Sparse Linear and Sparse Multiple Kernel MultiTask Learning". Changes:Initial Announcement on mloss.org.

About: Matlab implementation of variational gaussian approximate inference for Bayesian Generalized Linear Models. Changes:Code restructure and bug fix.

About: The package estimates the matrix of partial correlations based on different regularized regression methods: lasso, adaptive lasso, PLS, and Ridge Regression. Changes:Initial Announcement on mloss.org.
