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 matlab-like development environment.

• Add bibtex entry of corresponding Neurocomputing paper
• Create javascript modules to avoid global scope pollution in web pages

About: Kernel-based Learning Platform (KeLP) is Java framework that supports the implementation of kernel-based 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 kernel-machine algorithms. KeLP includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. It allows to build complex kernel machine based systems, leveraging on JSON/XML interfaces to instantiate prediction models without writing a single line of code.

In addition to minor improvements and bug fixes, this release includes:

• The possibility to generate the Compositional GRCT and the Compositional LCT data structures in kelp-input-generator.

• New metrics for evaluating Classification Tasks.

• New Tutorial and Unit Tests.

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 2.2.2!

About: pycobra is a python library for ensemble learning, which serves as a toolkit for regression, classification, and visualisation. It is scikit-learn compatible and fits into the existing scikit-learn ecosystem.

pycobra is further pep8 compliant, has improved tests and more plotting options.

About: The Weka workbench contains a collection of visualization tools and algorithms for data analysis and predictive modelling, together with graphical user interfaces for easy access to this [...]

This release include a lot of bug fixes and improvements. Some of these are detailed at

http://jira.pentaho.com/projects/DATAMINING/issues/DATAMINING-771

As usual, for a complete list of changes refer to the changelogs.

About: Kernel-Based Analysis of Biological Sequences

• importing apcluster package for avoiding method clashes
• improved and completed change history in inst/NEWS and package vignette

About: NaN-toolbox is a statistics and machine learning toolbox for handling data with and without missing values.

Changes in v.3.1.2 - improve configuration and build system - support of more platforms (including Octave 4.2.0) improved

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 gcc-5 and clang - fix typos

For details see the CHANGELOG at http://pub.ist.ac.at/~schloegl/matlab/NaN/CHANGELOG

About: General purpose Java Machine Learning library for classification, regression, and clustering.

See github release tab for change info

About: Toolkit for parametric and nonparametric regression and classification.

Initial Announcement on mloss.org.

About: RLScore - regularized least-squares machine learning algorithms package

Initial Announcement on mloss.org.

About: learn optimized scoring systems using MATLAB and the CPLEX Optimization Studio

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 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 user-friendly open source software in MATLAB and Octave and is freely available on the website.

New in toolbox

• Using GPU in Backpropagation
• Revision of some demo scripts
• Function approximation with multiple outputs
• Feature extraction with GRBM in first layer

About: Automatically finds the best model with its best parameter settings for a given classification or regression task.

Initial Announcement on mloss.org.

About: PCVM library a c++/armadillo implementation of the Probabilistic Classification Vector Machine.

30.10.2015 * code has been revised in some places fixing also some errors different multiclass schemes and hdf5 file support added. Some speed ups and memory savings by better handling of intermediate objects.

27.05.2015: - Matlab binding under Windows available. Added a solution file for VS'2013 express to compile a matlab mex binding. Can not yet confirm that under windows the code is really using multiple cores (under linux it does)

29.04.2015 * added an implementation of the Nystroem based PCVM includes: Nystroem based singular value decomposition (SVD), eigenvalue decomposition (EVD) and pseudo-inverse calculation (PINV)

22.04.2015 * implementation of the PCVM released

About: Apache Mahout is an Apache Software Foundation project with the goal of creating both a community of users and a scalable, Java-based framework consisting of many machine learning algorithm [...]

Apache Mahout introduces a new math environment we call Samsara, for its theme of universal renewal. It reflects a fundamental rethinking of how scalable machine learning algorithms are built and customized. Mahout-Samsara is here to help people create their own math while providing some off-the-shelf algorithm implementations. At its core are general linear algebra and statistical operations along with the data structures to support them. You can use is as a library or customize it in Scala with Mahout-specific extensions that look something like R. Mahout-Samsara comes with an interactive shell that runs distributed operations on a Spark cluster. This make prototyping or task submission much easier and allows users to customize algorithms with a whole new degree of freedom. Mahout Algorithms include many new implementations built for speed on Mahout-Samsara. They run on Spark 1.3+ and some on H2O, which means as much as a 10x speed increase. You’ll find robust matrix decomposition algorithms as well as a Naive Bayes classifier and collaborative filtering. The new spark-itemsimilarity enables the next generation of cooccurrence recommenders that can use entire user click streams and context in making recommendations.

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

• General:
  • Upgraded MTJ to 1.0.3.
• Common:
  • Added package for hash function computation including Eva, FNV-1a, MD5, Murmur2, Prime, SHA1, SHA2
  • Added callback-based forEach implementations to Vector and InfiniteVector, which can be faster for iterating through some vector types.
  • Optimized DenseVector by removing a layer of indirection.
  • Added method to compute set of percentiles in UnivariateStatisticsUtil and fixed issue with percentile interpolation.
  • Added utility class for enumerating combinations.
  • Adjusted ScalarMap implementation hierarchy.
  • Added method for copying a map to VectorFactory and moved createVectorCapacity up from SparseVectorFactory.
  • Added method for creating square identity matrix to MatrixFactory.
  • Added Random implementation that uses a cached set of values.
• Learning:
  • Implemented feature hashing.
  • Added factory for random forests.
  • Implemented uniform distribution over integer values.
  • Added Chi-squared similarity.
  • Added KL divergence.
  • Added general conditional probability distribution.
  • Added interfaces for Regression, UnivariateRegression, and MultivariateRegression.
  • Fixed null pointer exception that can happen in K-means with an empty cluster.
  • Fixed name of maxClusters property on AgglomerativeClusterer (was called maxMinDistance).
• Text:
  • Improvements to LDA Gibbs sampler.

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.

Initial Announcement on mloss.org.