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:20140411 Version 4.4 New features
Improvements
Plus bug fixes

About: Loglinear analysis for highdimensional data Changes:Initial Announcement on mloss.org.

About: Scalable tensor factorization Changes:

About: MOSIS is a modularized framework for signal processing, stream analysis, machine learning and stream mining applications. Changes:

About: The apcluster package implements Frey's and Dueck's Affinity Propagation clustering in R. The package further provides leveraged affinity propagation, exemplarbased agglomerative clustering, and various tools for visual analysis of clustering results. Changes:

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

About: "Ordinal Choquistic Regression" model using the maximum likelihood Changes:Initial Announcement on mloss.org.

About: ELKI is a framework for implementing datamining algorithms with support for index structures, that includes a wide variety of clustering and outlier detection methods. Changes:Additions and Improvements from ELKI 0.5.5: Algorithms Clustering:
Outlier detection
Distances
Database Layer and Data Types Projection layer * Parser for simple textual data (for use with Levenshtein distance) Various random projection families (including Feature Bagging, Achlioptas, and pstable) Latitude+Longitude to ECEF Sparse vector improvements and bug fixes New filter: remove NaN values and missing values New filter: add histogrambased jitter New filter: normalize using statistical distributions New filter: robust standardization using Median and MAD New filter: Linear discriminant analysis (LDA) Index Layer
Mathematics and Statistics
Visualization
Other

About: A scalable, fast C++ machine learning library, with emphasis on usability. Changes:

About: minFunc is a Matlab function for unconstrained optimization of differentiable realvalued multivariate functions using linesearch methods. It uses an interface very similar to the Matlab Optimization Toolbox function fminunc, and can be called as a replacement for this function. On many problems, minFunc requires fewer function evaluations to converge than fminunc (or minimize.m). Further it can optimize problems with a much larger number of variables (fminunc is restricted to several thousand variables), and uses a line search that is robust to several common function pathologies. 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: RLLib is a lightweight C++ template library that implements incremental, standard, and gradient temporaldifference learning algorithms in Reinforcement Learning. It is an optimized library for robotic applications that operates under fast duty cycles (e.g., ≤ 30 ms). RLLib has been tested and evaluated on Robocup 3D Soccer Simulation agents and physical NAO V4 humanoid robots to learn behaviors and represent learnable knowledge. Changes:Current release version is v1.6.

About: The GPML toolbox is a flexible and generic Octave 3.2.x and Matlab 7.x implementation of inference and prediction in Gaussian Process (GP) models. 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: A Matlab benchmarking toolbox for online and adaptive regression with kernels. Changes:Inclusion of Gaussian process based parameter estimation, and several new regression algorithms.

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: MLlib provides a distributed machine learning (ML) library to address the growing need for scalable ML. MLlib is developed in Spark (http://spark.incubator.apache.org/), a cluster computing system designed for iterative computation. Moreover, it is a component of a larger system called MLbase (www.mlbase.org) that aims to provide userfriendly distributed ML functionality both for ML researchers and domain experts. MLlib currently consists of scalable implementations of algorithms for classification, regression, collaborative filtering and clustering. Changes:Initial Announcement on mloss.org.

About: RLPy is a framework for performing reinforcement learning (RL) experiments in Python. RLPy provides a large library of agent and domain components, and a suite of tools to aid in experiments (parallelization, hyperparameter optimization, code profiling, and plotting). Changes:Initial Announcement on mloss.org.

About: pyGPs is a Python package for Gaussian process (GP) regression and classification for machine learning. Changes:pyGPs v1.1 is released. It replaces pyGP_OO and contains substaintal updates in functionality and documentation. pyGP_PR v1.1 is released with substantial documentation updates and renamed (FN > PR).

About: Variational Bayesian inference tools for Python Changes:Initial Announcement on mloss.org.
