RVowpalWabbit: R interface to the Vowpal Wabbit

R interface to Vowpal Wabbit fast out-of-core learning system The Vowpal Wabbit (VW) project is a fast out-of-core learning system sponsored by Yahoo! Research and written by John Langford along with a number of contributors.

There are two ways to have a fast learning algorithm: (a) start with a slow algorithm and speed it up, or (b) build an intrinsically fast learning algorithm. This project is about approach (b), and it has reached a state where it may be useful to others as a platform for research and experimentation.

There are several optimization algorithms available with the baseline being sparse gradient descent (GD) on a loss function (several are available). The code should be easily usable. Its only external dependence is on the Boost library, which is often installed by default.

This R package does not include the distributed computing implementation of the cluster/ directory of the upstream sources. Use of the software as a network servie is also not directly supported as the aim is a simpler direct call from R for validation and comparison.

Getting started

Package details

AuthorDirk Eddelbuettel <[email protected]>
MaintainerDirk Eddelbuettel <[email protected]>
LicenseGPL (>= 2)
URL https://github.com/JohnLangford/vowpal_wabbit/ http://dirk.eddelbuettel.com/code/rcpp.html
Package repositoryView on R-Forge
Installation Install the latest version of this package by entering the following in R:
install.packages("RVowpalWabbit", repos="http://R-Forge.R-project.org")

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RVowpalWabbit documentation built on May 31, 2017, 3:29 a.m.