School of Computer Science


Professor Gavin Brown

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Maintained by G.Brown

Software

Here are some Matlab tools I wrote - if you use them, please cite me.


NEW!
Cost Sensitive Boosting code [see the project homepage]
If you use this, please cite the following paper:
Cost Sensitive Boosting Algorithms: Do we really need them?
Nikolas Nikolaou, Meelis Kull, Narayanan Edakunni, Peter Flach, Gavin Brown
Machine Learning Journal. September 2016, Volume 104, Issue 2, pp 359-384.

PoEBoost. [download]
This is a demo toolbox covering our recent work on Boosting and Products of Experts. Some of the ideas were published in UAI 2011 (download the paper here). A fuller version, with extensions for online learning, is in submission to JMLR.


FEAST. [download].
This is a feature selection toolbox, providing implementations of common mutual information based filter feature selection algorithms. Click the link for more information.


MITOOLBOX. [download].
This is a toolbox written by my student Adam Pocock. It provides fast implementations of common mutual information functions such as entropy and conditional mutual information. This was originally implemented to support our research on feature selection - as such there are some demo files bundled with this implementing common information theoretic feature selection algorithms. These demos are early prototypes of the FEAST toolbox.

If you use this, please cite the following paper:
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
G.Brown, A.Pocock, M.Lujan, M.-J.Zhao
Journal of Machine Learning Research, vol 13, pages 27-66 (2012)