Welcome to the VFML (Very Fast Machine Learning) toolkit for mining high-speed data streams and very large data sets. VFML is made up of three main components. The first is a collection of tools and APIs that help a user develop new learning algorithms. The second component is a collection of implementations of important learning algorithms. The third component is a collection of scalable learning algorithms that were developed by Pedro Domingos and Geoff Hulten (with the help of several other people see Thanks). VFML is written in standard C (and a bit of Python), and provides a series of tutorials and examples as well as extensive in-source documentation in JavaDoc format. VFML is being distributed under a modified BSD license.
VFML provides code to help read and process training data, to gather sufficient statistics from it, ADTs for several important machine learning structures, and various helper code. You can get an overview of what is provided by visiting the Core APIs and Utility APIs sections of the documentation.
VFML contains a series of tools for working with data sets: cleaning them, sampling them, splitting them into train/test sets. It also has tools to help you experiment with learning algorithms. See the Other Tools documentation heading for more information.
VFML contains tools for learning decision trees, for learning the structure belief nets (aka Bayesian networks), and for clustering. Much of this code is easy to modify or extend (several other researchers have benefited from the bnlearn program, for example), and much of it can scale to learning from very large data sets or from data streams. You can get an overview of all the learners by checking out the Learning Programs section.
The following sections contain more detailed documentation about VFML's tool and learning programs.
The following sections contain links to the documentation for all of the APIs that you might find useful. You might like to download the reference manual (which contains all this information) in pdf format.
Hulten, G. and Domingos, P. "VFML -- A toolkit for mining high-speed time-changing data streams" http://www.cs.washington.edu/dm/vfml/. 2003.
Here is a BiBTeX entry:
@unpublished{VFML,
author = "Geoff Hulten and Pedro Domingos",
title = "{V}{F}{M}{L} -- A toolkit for mining high-speed time-changing data streams",
url = "http://www.cs.washington.edu/dm/vfml/",
year = 2003}
If you like, please also drop us a line about what you do with VFML and what results you obtain. We'd love to know, and it will help us in directing the future developments of VFML.
The official license information follows:
VFML - Very Fast Machine Learning toolkit Copyright (C) 2003, Geoff Hulten and Pedro Domingos All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of the University of Washington nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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