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 [...]

We are pleased to announce release 0.4 of Mahout. Virtually every corner of the project has changed, and significantly, since 0.3. Developers are invited to use and depend on version 0.4 even as yet more change is to be expected before the next release. Highlights include:

* Model refactoring and CLI changes to improve integration and consistency
* New ClusterEvaluator and CDbwClusterEvaluator offer new ways to evaluate clustering effectiveness
* New Spectral Clustering and MinHash Clustering (still experimental)
* New VectorModelClassifier allows any set of clusters to be used for classification
* Map/Reduce job to compute the pairwise similarities of the rows of a matrix using a customizable similarity measure
* Map/Reduce job to compute the item-item-similarities for item-based collaborative filtering
* RecommenderJob has been evolved to a fully distributed item-based recommender
* Distributed Lanczos SVD implementation
* More support for distributed operations on very large matrices
* Easier access to Mahout operations via the command line
* New HMM based sequence classification from GSoC (currently as sequential version only and still experimental)
* Sequential logistic regression training framework
* New SGD classifier
* Experimental new type of NB classifier, and feature reduction options for existing one
* New vector encoding framework for high speed vectorization without a pre-built dictionary
* Additional elements of supervised model evaluation framework
* Promoted several pieces of old Colt framework to tested status (QR decomposition, in particular)
* Can now save random forests and use it to classify new data
* Many, many small fixes, improvements, refactorings and cleanup

About: dANN is an Artificial Intelligence and Artificial Genetics library targeted at employing conventional techniques as well as acting as a platform for research & development of novel techniques. As new techniques are developed and proven to be effective they will be integrated into the core library. It is currently written in Java, C++, and C#. However only the java version is currently in active development. If you want to obtain a version other than the java version you will need to get it directly from GIT.

Please get the version in GIT only, the released version is old.

About: EANT Without Structural Optimization is used to learn a policy in either complete or partially observable reinforcement learning domains of continuous state and action space.

Initial Announcement on mloss.org.

About: The goal of the project is to provide a programming environment for easily exploring advanced topics in artificial intelligence and robotics without having to worry about the low-level details of [...]

Initial Announcement on mloss.org.