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 0.6.0: ELKI is now available on Maven: https://search.maven.org/#artifactdetailsde.lmu.ifi.dbs.elkielki0.7.0jar Please clone https://github.com/elkiproject/exampleelkiproject for a minimal project example. Uncertain data types, and clustering algorithms for uncertain data. Major refactoring of distances  removal of Distance values and removed support for nondoublevalued distance functions (in particular DoubleDistance was removed). While this reduces the generality of ELKI, we could remove about 2.5% of the codebase by not having to have optimized codepaths for doubledistance anymore. Generics for distances were present in almost any distancebased algorithm, and we were also happy to reduce the use of generics this way. Support for nondoublevalued distances can trivially be added again, e.g. by adding the specialization one level higher: at the query instead of the distance level, for example. In this process, we also removed the Generics from NumberVector. The objectbased get was deprecated for a good reason long ago, and e.g. doubleValue are more efficient (even for nonDoubleVectors). Dropped some longdeprecated classes. Kmeans:
CLARA clustering. Xmeans. Hierarchical clustering:
LSDBC clustering. EM clustering was refactored and moved into its own package. The new version is much more extensible. OPTICS clustering:
Outlier detection:
Parallel computation framework, and some parallelized algorithms
LibSVM format parser. kNN classification (with index acceleration). Internal cluster evaluation:
Statistical dependence measures:
Distance functions:
Preprocessing:
Indexing improvements:
Frequent Itemset Mining:
Uncertain clustering:
Mathematics:
MiniGUI has two "secret" new options: minigui.last minigui.autorun to load the last saved configuration and run it, for convenience. Logging API has been extended, to make logging more convenient in a number of places (saving some lines for progress logging and timing).

About: Kernelbased Learning Platform (KeLP) is Java framework that supports the implementation of kernelbased learning algorithms, as well as an agile definition of kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms, through the definition of specific interfaces. Once a new kernel function has been implemented, it can be automatically adopted in all the available kernelmachine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vectorbased to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate classifiers without writing a single line of code. Changes:This is a major release that includes brand new features as well as a renewed architecture of the entire project. Now KeLP is organized in four maven projects:
Furthermore this new release includes:
Check out this new version from our repositories. API Javadoc is already available. Your suggestions will be very precious for us, so download and try KeLP 2.0.0!

About: KernelBased Analysis of Biological Sequences Changes:

About: Variational Bayesian inference tools for Python Changes:

About: The Cognitive Foundry is a modular Java software library of machine learning components and algorithms designed for research and applications. Changes:

About: MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes (i) a javascript library to enable scientific computing within web pages, (ii) a javascript library implementing machine learning algorithms for classification, regression, clustering and dimensionality reduction, (iii) a web application providing a matlablike development environment. Changes:

About: KEEL (Knowledge Extraction based on Evolutionary Learning) is an open source (GPLv3) Java software tool that can be used for a large number of different knowledge data discovery tasks. KEEL provides a simple GUI based on data flow to design experiments with different datasets and computational intelligence algorithms (paying special attention to evolutionary algorithms) in order to assess the behavior of the algorithms. It contains a wide variety of classical knowledge extraction algorithms, preprocessing techniques (training set selection, feature selection, discretization, imputation methods for missing values, among others), computational intelligence based learning algorithms, hybrid models, statistical methodologies for contrasting experiments and so forth. It allows to perform a complete analysis of new computational intelligence proposals in comparison to existing ones. Moreover, KEEL has been designed with a twofold goal: research and educational. KEEL is also coupled with KEELdataset: a webpage that aims at providing to the machine learning researchers a set of benchmarks to analyze the behavior of the learning methods. Concretely, it is possible to find benchmarks already formatted in KEEL format for classification (such as standard, multi instance or imbalanced data), semisupervised classification, regression, time series and unsupervised learning. Also, a set of low quality data benchmarks is maintained in the repository. Changes:Initial Announcement on mloss.org.

About: Nowadays, this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many ability like feature extraction and classification that are used in many applications like image processing, speech processing and etc. According to the results of the experiments conducted on MNIST (image), ISOLET (speech), and 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. 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, etc. The toolbox is a userfriendly open source software and is freely available on the website. Changes:New in toolbox

About: The Universal Java Matrix Package (UJMP) is a data processing tool for Java. Unlike JAMA and Colt, it supports multithreading and is therefore much faster on current hardware. It does not only support matrices with double values, but instead handles every type of data as a matrix through a common interface, e.g. CSV files, Excel files, images, WAVE audio files, tables in SQL data bases, and much more. Changes:Updated to version 0.3.0

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:20150709 Version 4.6 Development and release branches available at https://github.com/gpstuffdev/gpstuff New features
Improvements  faster EP moment calculation for lik_logit Several minor bugfixes

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: R package implementing statistical test and post hoc tests to compare multiple algorithms in multiple problems. Changes:Initial Announcement on mloss.org.

About: Simple and hopefully clean and easy to follow implementation of the Generalized Learning Vector Quantizer (GLVQ) with variants for metric adaptation (RGLVQ, GMLVQ, LiRaM). Changes:Initial Announcement on mloss.org.

About: MIPS is a software library for stateoftheart graph mining algorithms. The library is platform independent, written in C++(03), and aims at implementing generic and efficient graph mining algorithms. Changes:description update

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: A Theano framework for building and training neural networks Changes:Initial Announcement on mloss.org.

About: A toolkit for hyperparameter optimization for machine learning algorithms. Changes:Initial Announcement on mloss.org.

About: Mulan is an opensource Java library for learning from multilabel datasets. Multilabel datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multilabel dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions. Changes:Learners
Measures/Evaluation
Bug fixes
API changes
Miscalleneous

About: MALSS is a python module to facilitate machine learning tasks. Changes:Initial Announcement on mloss.org.
