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Learning Tree Structures for Conditional Random Fields (CRFs)

This page provides code from my project on learning tree structures for Conditional Random Fields (CRFs). This code is a somewhat improved version of the code used for this paper:
Joseph K. Bradley and Carlos Guestrin. Learning Tree Conditional Random Fields. International Conference on Machine Learning (ICML), 2010.
bibtex/abstract PDF

Note: This code is part of a larger SELECT Lab codebase which is available but still being improved for actual release. See my SILL Project Page for info on that codebase.

Page contents:

Project Overview

Main goals: Approach:

CRF Learning Code

Download: The code is available here as a gzipped tar file.

Code Overview

Main parts of code: About the code:

Installation and Getting Started

The code has detailed instructions on how to install the necessary dependencies and build our code. Once you download the code, look at these files in the home directory:
introduction, installation, getting started
licensing information
list of contributors
info on duplicating my experiments
Doxygen-generated documentation


This code is mostly released under the GNU General Public License (GPL). However, a few files are released under the GNU Lesser General Public License (LGPL). See the LICENSE.txt file for more details. The code is a subset of the SELECT Lab's larger codebase. We are planning to release the entire codebase under a more permissive license before long.

If You Have Questions

If you have questions, get weird results, etc., please feel free to contact me; my email is listed at the top of my homepage. If you find bugs, definitely contact me! :)