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: DIANNE is a modular software framework for designing, training and evaluating artificial neural networks on heterogeneous, distributed infrastructure . It is built on top of OSGi and AIOLOS and can transparently deploy and redeploy (parts of) a neural network on multiple machines, as well as scale up training on a compute cluster.

Initial Announcement on mloss.org.

About: A Java Toolbox for Scalable Probabilistic Machine Learning.

• Added sparklink module implementing the integration with Apache Spark. More information here.
• Fluent pattern in latent-variable-models
• Predefined model implementing the concept drift detection

Detailed information can be found in the toolbox's web page

About: Analytic engine for real-time large-scale streams containing structured and unstructured data.

Initial Announcement on mloss.org.

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

• Move "flow"-related classes into package "de.claas.mosis.flow" (e.g. Node and Link).
• Refined and improved "flow"-related tests (e.g. Iterator and Node tests).
• Refactored tests for data formats (e.g. PlainText and JSON tests).
• Added visitor design pattern for graph-based functions (e.g. initialization and processing).
• Documented parameters of Processor implementations.

About: A collection of python code to perform research in optimization. The aim is to provide reusable components that can be quickly applied to machine learning problems. Used in: - Ellipsoidal multiple instance learning - difference of convex functions algorithms for sparse classfication - Contextual bandits upper confidence bound algorithm (using GP) - learning output kernels, that is kernels between the labels of a classifier.

• minor bugfix