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

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 2.0.1!

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: 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: 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 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: 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.

About: Bayesian Logic (BLOG) is a probabilistic modeling language. It is designed for representing relations and uncertainties among real world objects. Changes:Initial Announcement on mloss.org.

About: Jmlp is a java platform for both of the machine learning experiments and application. I have tested it on the window platform. But it should be applicable in the linux platform due to the crossplatform of Java language. It contains the classical classification algorithm (Discrete AdaBoost.MH, Real AdaBoost.MH, SVM, KNN, MCE,MLP,NB) and feature reduction(KPCA,PCA,Whiten) etc. Changes:Initial Announcement on mloss.org.

About: Hivemall is a scalable machine learning library running on Hive/Hadoop. Changes:

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
