About: The GPML toolbox is a flexible and generic Octave/Matlab implementation of inference and prediction with Gaussian process models. The toolbox offers exact inference, approximate inference for nonGaussian likelihoods (Laplace's Method, Expectation Propagation, Variational Bayes) as well for large datasets (FITC, VFE, KISSGP). A wide range of covariance, likelihood, mean and hyperprior functions allows to create very complex GP models. Changes:A major code restructuring effort did take place in the current release unifying certain inference functions and allowing more flexibility in covariance function composition. We also redesigned the whole derivative computation pipeline to strongly improve the overall runtime. We finally include gridbased covariance approximations natively. More generic sparse approximation using Power EP
Approximate covariance object unifying sparse approximations, gridbased approximations and exact covariance computations
Hiearchical structure of covariance functions
Faster derivative computations for mean and cov functions
New mean functions
New optimizer
New GLM link function
Smaller fixes

About: NaNtoolbox is a statistics and machine learning toolbox for handling data with and without missing values. Changes:Changes in v.3.0.3  improve compatibility for Octave on Windows Changes in v.3.0.1  fix packaging for octave Changes in v.2.8.5  bug fix: trimmean  compiler support for gcc5 and clang  fix typos For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG

About: Nowadays this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use a stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many abilities such as feature extraction and classification that are used in many applications including image processing, speech processing, text categorization, etc. This paper introduces a new object oriented toolbox with the most important abilities needed for the implementation of DBNs. According to the results of the experiments conducted on the MNIST (image), ISOLET (speech), and the 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. Also on all the aforementioned datasets, the obtained classification errors are comparable to those of the state of the art classifiers. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), GPU based, etc. The toolbox is a userfriendly open source software in MATLAB and Octave and is freely available on the website. Changes:New in toolbox

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems. Changes:

About: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. Changes:20160609 Version 4.7 Development and release branches available at https://github.com/gpstuffdev/gpstuff New features
Improvements
Bugfixes

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, Renyi entropy/mutual information, and weighted variants of Shannon entropies/mutual informations. Works with discrete distributions, and expects column vectors of features. Changes:Relicensed as BSD. Added checks to catch MATLAB inputs that aren't doubles.

About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinearoptimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python. Changes:Fixed bug in save/restore. Fixed bug in initial design.

About: Optunity is a library containing various optimizers for hyperparameter tuning. Hyperparameter tuning is a recurrent problem in many machine learning tasks, both supervised and unsupervised.This package provides several distinct approaches to solve such problems including some helpful facilities such as crossvalidation and a plethora of score functions. Changes:This minor release has the same feature set as Optunity 1.1.0, but incorporates several bug fixes, mostly related to the specification of structured search spaces.

About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields. Changes:Release 0.3.2 fixes various bugs and adds GLC (Generalized Loop Corrections) written by Siamak Ravanbakhsh.

About: This Matlab package implements a method for learning a choquistic regression model (represented by a corresponding Moebius transform of the underlying fuzzy measure), using the maximum likelihood approach proposed in [2], eqquiped by sigmoid normalization, see [1]. Changes:Initial Announcement on mloss.org.

About: Toeblitz is a MATLAB/Octave package for operations on positive definite Toeplitz matrices. It can solve Toeplitz systems Tx = b in O(n*log(n)) time and O(n) memory, compute matrix inverses T^(1) (with free log determinant) in O(n^2) time and memory, compute log determinants (without inverses) in O(n^2) time and O(n) memory, and compute traces of products A*T for any matrix A, in minimal O(n^2) time and memory. Changes:Adding a writeup in written/toeblitz.pdf describing the package.

About: This package is an implementation of a linear RankSVM solver with nonconvex regularization. Changes:Initial Announcement on mloss.org.

About: The package computes the optimal parameters for the Choquet kernel Changes:Initial Announcement on mloss.org.

About: LIBOL is an opensource library with a family of stateoftheart online learning algorithms for machine learning and big data analytics research. The current version supports 16 online algorithms for binary classification and 13 online algorithms for multiclass classification. Changes:In contrast to our last version (V0.2.3), the new version (V0.3.0) has made some important changes as follows: • Add a template and guide for adding new algorithms; • Improve parameter settings and make documentation clear; • Improve documentation on data formats and key functions; • Amend the "OGD" function to use different loss types; • Fixed some name inconsistency and other minor bugs.

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: GPgrid toolkit for fast GP analysis on grid input Changes:Initial Announcement on mloss.org.

About: Fast Multidimensional GP Inference using Projected Additive Approximation Changes:Initial Announcement on mloss.org.

About: Stochastic neighbor embedding originally aims at the reconstruction of given distance relations in a lowdimensional Euclidean space. This can be regarded as general approach to multidimensional scaling, but the reconstruction is based on the definition of input (and output) neighborhood probability alone. The present implementation also allows for handling dissimilarity or scoreinduced neighborhood topologies and makes use of quasi 2nd order gradientbased (l)BFGS optimization. Changes:

About: Approximate Rank One FACtorization of tensors. An algorithm for factorization of threewaytensors and determination of their rank, includes example applications. Changes:Initial Announcement on mloss.org.
