Projects that are tagged with distributed machine learning.


Logo SMPyBandits 0.9.2

by Naereen - March 20, 2018, 20:12:13 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2548 views, 477 downloads, 0 subscriptions

About: SMPyBandits: an Open-Source Research Framework for Single and Multi-Players Multi-Arms Bandits (MAB) Algorithms in Python

Changes:

Initial Announcement on mloss.org.


Logo SAMOA 0.0.1

by gdfm - April 2, 2014, 17:09:08 CET [ Project Homepage BibTeX Download ] 3489 views, 971 downloads, 0 subscriptions

About: SAMOA is a platform for mining big data streams. It is a distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms.

Changes:

Initial Announcement on mloss.org.


Logo Jubatus 0.5.0

by hido - November 30, 2013, 17:41:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8444 views, 1454 downloads, 0 subscriptions

About: Jubatus is a general framework library for online and distributed machine learning. It currently supports classification, regression, clustering, recommendation, nearest neighbors, anomaly detection, and graph analysis. Loose model sharing provides higher scalability, better performance, and real-time capabilities, by combining online learning with distributed computations.

Changes:

0.5.0 add new supports for clustering and nearest neighbors. For more detail, see http://t.co/flMcTcYZVs


Logo MLlib 0.8

by atalwalkar - October 10, 2013, 00:56:25 CET [ Project Homepage BibTeX Download ] 5812 views, 1150 downloads, 0 subscriptions

About: MLlib provides a distributed machine learning (ML) library to address the growing need for scalable ML. MLlib is developed in Spark (http://spark.incubator.apache.org/), a cluster computing system designed for iterative computation. Moreover, it is a component of a larger system called MLbase (www.mlbase.org) that aims to provide user-friendly distributed ML functionality both for ML researchers and domain experts. MLlib currently consists of scalable implementations of algorithms for classification, regression, collaborative filtering and clustering.

Changes:

Initial Announcement on mloss.org.