Machine Learning Framework

  •        0

Machine Learning Framework (MLF) is a library based on .NET Framework for machine learning implementation. This library consists of collection of machine learning algorithms such as Bayesian, Neural Network, SOM, Genetic Algorithm, SVM, and etc.



comments powered by Disqus

Related Projects

Scikit Learn - Machine Learning in Python

scikit-learn is a Python module for machine learning built on top of SciPy. It is simple and efficient tools for data mining and data analysis. It supports automatic classification, clustering, model selection, pre processing and lot more.

Apache Mahout - Scalable machine learning library

Apache Mahout has implementations of a wide range of machine learning and data mining algorithms: clustering, classification, collaborative filtering and frequent pattern mining.

HPCC System - Hadoop alternative

HPCC is a proven and battle-tested platform for manipulating, transforming, querying and data warehousing Big Data. It supports two type of configuration. Thor is responsible for consuming vast amounts of data, transforming, linking and indexing that data. It functions as a distributed file system with parallel processing power spread across the nodes. Roxie, the Data Delivery Engine, provides separate high-performance online query processing and data warehouse capabilities.

PredictionIO - Machine Learning Server

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. It helps to predict user behaviors.

MLIB - Apache Spark's scalable machine learning library

MLlib is a Spark implementation of some common machine learning algorithms and utilities, including classification, regression, clustering, collaborative filtering, dimensionality reduction and lot more.

Maja Machine Learning Framework

This project provides a framework for testing and comparing different machine learning algorithms (particularly reinforcement learning methods) in different scenarios. Its intended area of application is in research and education.


An open-source C++ library of machine learning by New York University's machine learning lab, led by Yann LeCun. In particular, implementations of convolutional neural networks with energy-based models along with a GUI, demos and tutorials.

OpenFst Library for constructing weighted finite-state transducer

OpenFst is a library for constructing, combining, optimizing, and searching weighted finite-state transducers (FSTs). Weighted finite-state transducers are automata where each transition has an input label, an output label, and a weight. FSTs have key applications in speech recognition and synthesis, machine translation, optical character recognition, pattern matching, string processing, machine learning, information extraction and retrieval among others.

Bayesian Network Classifiers in Java

jBNC is a Java toolkit for training, testing, and applying Bayesian Network Classifiers. Implemented classifiers have been shown to perform well in a variety of artificial intelligence, machine learning, and data mining applications.


Now part of Apache's Mahout machine learning project at please see there for latest info and code and releases and support!