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Python module integrating various machine learning algorithms under a common interface. It offers a wide range of methods such as Support Vector Machines, linear models (L1, L2 penalized), logistic regression, gaussian mixture models and more. The large number of algorithms aleady implemented allows ... [More]
Apache Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. ... [More]
The SHOGUN machine learning toolbox's focus is on large scale kernel methods and especially on Support Vector Machines (SVM). It comes with a generic interface for SVMs, features several SVM and kernel implementations, includes LinAdd optimizations and also Multiple Kernel Learning algorithms. ... [More]
Accord.NET Framework is a C# framework which extends the excellent AForge.NET Framework with new tools and libraries. The framework is comprised by libraries and sample applications demonstrating their features. Some of the libraries include: Accord.Statistics - library with statistical ... [More]
A framework for learning from a continuous supply of examples, a data stream. Includes classification and clustering methods. Related to the WEKA project, also written in Java, while scaling to more demanding problems.
PyBrain is a modular Machine Learning Library for Python. It's goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement ... [More]
Myrrix is a complete, real-time, scalable recommender system, evolved from Apache Mahout™. Just as we take for granted easy access to powerful, economical storage and computing today, Myrrix will let you take for granted easy access to large-scale learning from data. The Serving Layer component ... [More]
DL-Learner is a tool for learning concepts in Description Logics (DLs) from user-provided examples. Equivalently, it can be used to learn classes in OWL ontologies from selected objects.
Cascading is a feature rich API for defining and executing complex and fault tolerant data processing workflows on a Hadoop cluster.