About: A mutual information library for C and Mex bindings for MATLAB. Aimed at feature selection, and provides simple methods to calculate mutual information, conditional mutual information, entropy, conditional entropy, and Renyi entropy/mutual information. Works with discrete distributions, and expects column vectors of features. Changes:Updated documentation & link to JMLR publication.

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. Changes:Initial Announcement on mloss.org.

About: NaNtoolbox is a statistics and machine learning toolbox for handling data with and without missing values. Changes:Changes in v.2.5.2  faster version of quantile if multiple quantiles are requested  removes the dependency on ZLIB and thus  fixes "pkg install nan" for Octave on Windows  a number of minor improvements For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG

About: Locally Weighted Projection Regression (LWPR) is a recent algorithm that achieves nonlinear function approximation in high dimensional spaces with redundant and irrelevant input dimensions. At its [...] Changes:Version 1.2.4

About: A Matlab implementation of Sparse PCA using the inverse power method for nonlinear eigenproblems. Changes:Initial Announcement on mloss.org.

About: MATLAB toolbox for advanced BrainComputer Interface (BCI) research. Changes:Initial Announcement on mloss.org.

About: This is a Matlab/C++ "toolbox" of code for learning and inference with graphical models. It is focused on parameter learning using marginalization in the hightreewidth setting. Changes:Initial Announcement on mloss.org.

About: Multicore/distributed large scale machine learning framework. Changes:Update version.

About: FLANN is a library for performing fast approximate nearest neighbor searches in high dimensional spaces. It contains a collection of algorithms we found to work best for nearest neighbor search. Changes:See project page for changes.

About: Denoising images via normalized convolution Changes:Initial Announcement on mloss.org.

About: Multiclass vector classification based on cost functiondriven learning vector quantization , minimizing misclassification. Changes:Initial Announcement on mloss.org.

About: Bayesian Reasoning and Machine Learning toolbox Changes:Fixed some small bugs and updated some demos.

About: Correlative Matrix Mapping (CMM) provides a supervised linear data mapping into a Euclidean subspace of given dimension. Applications include denoising, visualization, labelspecific data preprocessing, and assessment of data attribute pairs relevant for the supervised mapping. Solving autoassociation problems yields linear multidimensional scaling, similar to PCA, but usually with more faithful lowdimensional mappings. Changes:Tue Jul 5 14:40:03 CEST 2011  Bugfixes and cleanups

About: A fast and scalable graphbased clustering algorithm based on the eigenvectors of the nonlinear 1Laplacian. Changes:

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

About: The software provides an implementation of a filter/smoother based on Gibbs sampling, which can be used for inference in dynamical systems. Changes:Initial Announcement on mloss.org.

About: OpenGM is a free C++ template library, a command line tool and a set of MATLAB functions for optimization in higher order graphical models. Graphical models of any order and structure can be built either in C++ or in MATLAB, using simple and intuitive commands. These models can be stored in HDF5 files and subjected to stateoftheart optimization algorithms via the OpenGM command line optimizer. All library functions can also be called directly from C++ code. OpenGM realizes the Inference Algorithm Interface (IAI), a concept that makes it easy for programmers to use their own algorithms and factor classes with OpenGM. Changes:Initial Announcement on mloss.org.

About: The SUMO Toolbox is a Matlab toolbox that automatically builds accurate surrogate models (also known as metamodels or response surface models) of a given data source (e.g., simulation code, data set, script, ...) within the accuracy and time constraints set by the user. The toolbox minimizes the number of data points (which it selects automatically) since they are usually expensive. Changes:Incremental update, fixing some cosmetic issues, coincides with JMLR publication.

About: The gmm toolbox contains code for density estimation using mixtures of Gaussians: Starting from simple kernel density estimation with spherical and diagonal Gaussian kernels over manifold Parzen window until mixtures of penalised full Gaussians with only a few components. The toolbox covers many Gaussian mixture model parametrisations from the recent literature. Most prominently, the package contains code to use the Gaussian Process Latent Variable Model for density estimation. Most of the code is written in Matlab 7.x including some MEX files. Changes:Initial Announcement on mloss.org

About: PSVM  Support vector classification, regression and feature extraction for nonsquare dyadic data, nonMercer kernels. Changes:Initial Announcement on mloss.org.
