About: A java library for the analysis of data streams with probabilistic graphical models. AMIDST provides parallel multicore implementations of Bayesian parameter learning algorithms using variational message passing and importance sampling for static and dynamic Bayesian networks. Additionally, AMIDST efficiently leverages existing functionalities and algorithms by interfacing to software tools such as Weka, Moa, HUGIN and R. Changes:

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 [...] Changes:In core weka:
In packages:

About: Jatecs is an open source Java library focused on automatic text categorization. Changes:Initial Announcement on mloss.org.

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls. Changes:

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 0.7.0 to 0.7.1: Algorithm additions:
Important bug fixes:
UI improvements:
Smaller changes:

About: General purpose Java Machine Learning library for classification, regression, and clustering. Changes:Initial Announcement on mloss.org.

About: A Java framework for statistical analysis and classification of biological sequences Changes:New classes and packages:
New features and improvements:

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 prediction models without writing a single line of code. Changes:In addition to minor bug fixes, this 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 2.0.2!

About: The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining realworld, complex knowledge workflows. Changes:Some highlights of this release:

About: Software for Automatic Construction and Inference of DBNs Based on Mathematical Models Changes:Initial Announcement on mloss.org.

About: Easily prototype WEKA classifiers and filters using Python scripts. Changes:0.3.0
0.2.1
0.2.0
0.1.1
0.1.0

About: Apache Mahout is an Apache Software Foundation project with the goal of creating both a community of users and a scalable, Javabased framework consisting of many machine learning algorithm [...] Changes: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. MahoutSamsara is here to help people create their own math while providing some offtheshelf 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 Mahoutspecific extensions that look something like R. MahoutSamsara 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 MahoutSamsara. 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 sparkitemsimilarity 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. 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: The Java package jLDADMM is released to provide alternative choices for topic modeling on normal or short texts. It provides implementations of the Latent Dirichlet Allocation topic model and the onetopicperdocument Dirichlet Multinomial Mixture model (i.e. mixture of unigrams), using collapsed Gibbs sampling. In addition, jLDADMM supplies a document clustering evaluation to compare topic models. Changes:Initial Announcement on mloss.org.

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: Rival is an open source Java toolkit for recommender system evaluation. It provides a simple way to create evaluation results comparable across different recommendation frameworks. Changes:Initial Announcement on mloss.org.

About: Incremental (Online) Nonparametric Classifier. You can classify both points (standard) or matrices (multivariate time series). Java and Matlab code already available. Changes:version 2: parameterless system, constant model size, prediction confidence (for active learning). NEW!! C++ version at: https://github.com/ilariagori/ABACOC

About: FAST is an implementation of Hidden Markov Models with Features. It allows features to modify both emissions and transition probabilities. Changes:Initial Announcement on mloss.org.
