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.

About: An implementation of the infinite hidden Markov model. Changes:Since 0.4: Removed dependency from lightspeed (now using statistics toolbox). Updated to newer matlab versions.

About: The library implements Optimized Cutting Plane Algorithm (OCAS) for efficient training of linear SVM classifiers from largescale data. Changes:Implemented COFFIN framework which allows efficient training of invariant image classifiers via virtual examples.

About: Toolbox for circular statistics with Matlab (The Mathworks). Changes:Some bugfixes.

About: This software is an implementation of Hidden Markov Support Vector Machines (HMSVMs). Changes:Initial Announcement on mloss.org.

About: This software is designed for learning translation invariant kernels for classification with support vector machines. Changes:Initial Announcement on mloss.org.

About: This toolbox provides functions for maximizing and minimizing submodular set functions, with applications to Bayesian experimental design, inference in Markov Random Fields, clustering and others. Changes:

About: RLS2 is an instance of multiple kernel learning algorithm to simultaneously learn a regularized predictor and the kernel function. RLS2LIN is a version of RLS2 specialized to linear kernels on each feature. The package contains a set of scripts that implements RLS2 and RLS2LIN, together with a Graphic User Interface to load data, perform training, validation, and plot results. Changes:

About: Orthonormal wavelet transform for D dimensional tensors in L levels. Generic quadrature mirror filters and tensor sizes. Runtime is O(n), plain C, MEXwrapper and demo provided. Changes:Initial Announcement on mloss.org. 
About: The open source ErrorCorrecting Output Codes (ECOC) library contains both stateoftheart coding and decoding designs, as well as the option to include your own coding, decoding, and base classifier. Changes:Initial Announcement on mloss.org.

About: LIBSVM is an integrated software for support vector classification, (CSVC, nuSVC ), regression (epsilonSVR, nuSVR) and distribution estimation (oneclass SVM). It supports multiclass [...] Changes:Initial Announcement on mloss.org.

About: GPUML is a library that provides a C/C++ and MATLAB interface for speeding up the computation of the weighted kernel summation and kernel matrix construction on GPU. These computations occur commonly in several machine learning algorithms like kernel density estimation, kernel regression, kernel PCA, etc. Changes:Initial Announcement on mloss.org.
