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MLO Group  /   Dr Gavin Brown

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Maintained by G.Brown

Publications



You can see most (if not all) of these on my Google Scholar profile.


2014

Conferences

Statistial Hypothesis Testing in Positive Unlabeled Data
Konstantinos Sechidis, Borja Calvo and Gavin Brown
European Conference on Machine Learning (ECML). France, Sept 2014. Acceptance rate 115/483 (23.8%)

Information theoretic feature selection in multi-label data through composite likelihood
Konstantinos Sechidis, Nikolas Nikoloau, Gavin Brown
Intl. Workshop Statistical, Syntactic and Structural Pattern Recognition (SSPR). August 2014

Predicting Performance of OWL reasoners: Locally or Globally?
Viachaslau Sazonau, Uli Sattler, Gavin Brown
14th International Conference on Principles of Knowledge Representation and Reasoning (KR 2014), Vienna, Austria, AAAI Press.

2013

Journal articles

Beyond Fano's Inequality: Bounds on the Optimal F-Score, BER, and Cost-Sensitive Risk and Their Implications [PDF] [GoogleScholar]
Ming-Jie Zhao, Narayanan Edakunni, Adam Pocock and Gavin Brown
Journal of Machine Learning Research. Volume 14, pages 1033--1090, (2013)

Random Projection Random Discretization Ensembles -- Ensembles of Linear Multivariate Decision Trees [PDF] [GoogleScholar]
Amir Ahmad and Gavin Brown
IEEE Transactions on Knowledge and Data Engineering. Accepted, in press for 2013.

Optimizing Software Runtime Systems for Speculative Parallelization
Paraskevas Yiapannis, Demian Rosas Gavin Brown, Mikel Lujan
Transactions on Architecture and Code Optimization. Volume 9 Issue 4, January 2013

Conferences

Exploring Sketches for Probability Estimation with Sublinear Memory
Anthony Kleerekoper, Mikel Lujan, and Gavin Brown
IEEE Conference on Big Data 2013.

ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge [PDF] [GoogleScholar]
Michele Filannino, Gavin Brown, and Goran Nenadic
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Proceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013). Atlanta, Georgia, USA. June 2013

2012

Journal articles

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection [PDF] [GoogleScholar]
Gavin Brown, Adam Pocock, Mingjie Zhao, Mikel Lujan
Journal of Machine Learning Research. Volume 13 (2012), pages 27-66

See also our FEAST toolkit [DOWNLOAD], which includes datasets from the above paper.


Conferences

Informative Priors for Markov Blanket Discovery [PDF] [GoogleScholar]
Adam Pocock, Mikel Lujan, Gavin Brown
International Conference on Artificial Intelligence and Statistics. La Palma, April 2012

2011

Invited Contributions

From Heuristics to Statistics (keynote address)
Gavin Brown
IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments. Paris, France, April 2011

Conferences

Boosting as a Product of Experts [PDF] [GoogleScholar]
Narayanan Edakunni, Gavin Brown, Tim Kovacs
Uncertainty in Artificial Intelligence (UAI). July 2011

Garbage Collection Auto-Tuning for Java MapReduce on Multi-Cores [PDF] [GoogleScholar]
Jeremy Singer, George Kovoor, Gavin Brown, Mikel Lujan
Proceedings of the International Symposium on Memory Management. June 2011

Theoretical and Empirical Analysis of Diversity in Non-Stationary Learning [PDF] [GoogleScholar]
Richard Stapenhurst and Gavin Brown
IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments. Paris, France, April 2011

Online, GA based Mixture of Experts : a Probabilistic Model of UCS [PDF] [GoogleScholar]
Nara Edakunni, Gavin Brown, Tim Kovacs
Proceedings of the Genetic and Evolutionary Computation COnference (GECCO). July 2011

Accuracy Exponentiation in UCS and its Effect on Voting Margins [PDF] [GoogleScholar]
Tim Kovacs, Nara Edakunni, Gavin Brown
Proceedings of the Genetic and Evolutionary Computation COnference (GECCO). July 2011



2010

Journal / Book chapters

Ensemble Learning [PDF] [GoogleScholar]
Gavin Brown
Encyclopaedia of Machine Learning. C.Sammut & G.I.Webb (Eds.) Springer, ISBN 0-387-307-680

Learn++.MF : A Random Subspace Approach for the Missing Feature Problem [PDF] [GoogleScholar]
Polikar R., DePasquale J., Syed Mohammed H., Brown G., Kuncheva L.I.
Pattern Recognition. (in press, 2010)

Invited Contributions

Some thoughts at the interface of Ensemble Methods and Feature Selection [Slides]
Gavin Brown
Plenary at Intl Workshop on Multiple Classifier Systems. Cairo, April 2010

Conferences

The Economics of Garbage Collection [PDF] [GoogleScholar]
J. Singer, R. Jones, G. Brown and M. Lujan
Intl Symposium on Memory Management. June 2010

Toward a More Accurate Understanding of the Limits of the TLS Execution Paradigm
N.Ioannou, J.Singer, S.Khan, P.Xekalakis, P.Yiapanis, A.Pocock, G.Brown, M.Luj?, I.Watson, and M.Cintra
Intl Symposium on Workload Characterization (IISWC). Dec 2010

Good and Bad Diversity in Majority Vote Ensembles [PDF] [GoogleScholar] [Slides]
Gavin Brown and Ludmila Kuncheva
Intl Workshop on Multiple Classifier Systems. Cairo, April 2010

Online Nonstationary Boosting [PDF] [GoogleScholar]
Adam Pocock, Paraskevas Yiapanis, Jeremy Singer, Mikel Lujan, and Gavin Brown
Intl Workshop on Multiple Classifier Systems. Cairo, April 2010

Static Java Program Features for Intelligent Squash Prediction
Jeremy Singer, Paraskevas Yiapanis, Adam Pocock, Mikel Luj?, Gavin Brown, Nikolas Ioannou and Marcelo Cintra
Proceedings of the 4th Workshop on Statistical and Machine Learning Approaches to Architecture and Compilation (SMART). Jan 2010

Analytic Solutions to Differential Equations under Graph-based Genetic Programming [PDF] [GoogleScholar]
Tom Seaton, Gavin Brown and Julian Miller
13th European Conference on Genetic Programming (EuroGP). Istanbul, 2010

Non Peer reviewed

Windows Shut on Curiosity
Gavin Brown
The Times Higher Education Magazine. 3rd June 2010 [LINK]



2009

Invited Contributions

Feature Selection by Filters: A Unifying Perspective
Gavin Brown
Plenary Lecture at UK Symposium on Knowledge Discovery and Data Mining. Salford, June 2009

Conferences

A New Perspective for Information Theoretic Feature Selection [PDF] [GoogleScholar]
Gavin Brown
Twelfth International Conference on Artificial Intelligence and Statistics. Florida, June 2009

A Space of Feature Selectors based on Multivariate Mutual Information
Gavin Brown
Sparsity in Machine Learning and Statistics. Cumberland Lodge, UK, April 2009

Modeling UCS as a Mixture of Experts [PDF] [GoogleScholar]
Nara Edakunni, Tim Kovacs, Gavin Brown, James Marshall, Arjun Chandra
Proceedings of the Genetic and Evolutionary Computation COnference (GECCO). Montreal, Canada, July 2009

An Information Theoretic Perspective on Multiple Classifier Systems [PDF] [GoogleScholar]
Gavin Brown
Intl Workshop on Multiple Classifier Systems. Iceland, June 2009

A Study on Semi-Supervised Generative Ensembles
Manuela Zanda and Gavin Brown
Intl Workshop on Multiple Classifier Systems. Iceland, June 2009

Random Ordinality Ensembles : A Novel Ensemble Method for Multi-Valued Categorical Data [PDF] [GoogleScholar]
Amir Ahmad and Gavin Brown
Intl Workshop on Multiple Classifier Systems. Iceland, June 2009

Random Linear Oracle: An Ensemble Method for Low-Variance Classifiers
Amir Ahmad and Gavin Brown
Intl Workshop on Multiple Classifier Systems. Iceland, June 2009

Fundamental Nano-Patterns to Characterize and Classify Java Methods
Jeremy Singer, Gavin Brown, Mikel Luj?, Adam Pocock and Paraskevas Yiapanis
Intl Workshop Language Descriptions, Tools and Applications (LDTA). March 2009



2008

Conferences

Biomarker Selection in Non-Small Cell Lung Cancer
Zulkifli Dol, Gavin Brown, John Field, Andy Brass
Intl Workshop on Lung Cancer. Liverpool, July 2008

An Information Theoretic Evaluation of Software Metrics for Object Lifetime Prediction [PDF] [GoogleScholar]
Sebastien Marion, Gavin Brown, Richard Jones, Mikel Luj?, Chris Ryder and Ian Watson
Workshop on Statistical and Machine learning approaches applied to ARchitectures and compilaTion. Jan 2008



2007

Journal / Book chapters

Sparse Distributed Memory using Rank Order Neural Codes [PDF] [GoogleScholar]
Steve Furber, Gavin Brown, Joy Bose, Mike Cumpstey, Peter Marshall, Jon Shapiro
IEEE Transactions on Neural Networks.. Vol 18, issue 3, May 2007.

Return Value Prediction meets Information Theory [PDF] [GoogleScholar]
Jeremy Singer and Gavin Brown
Journal of Electronic Notes in Theoretical Computer Science (Special Issue on Quantitative Aspects of Programming Languages). Volume 164, Issue 3, pg 137-151, 2007

Conferences

Bayesian Estimation of Rule Accuracy in UCS [PDF] [GoogleScholar]
James Marshall, Gavin Brown, Tim Kovacs
Proceedings of the Genetic and Evolutionary Computation COnference (GECCO). July 2007

UCSpv: Principled Voting in UCS Rule Populations [PDF] [GoogleScholar]
Gavin Brown, Tim Kovacs, James Marshall
Proceedings of the Genetic and Evolutionary Computation COnference (GECCO) (37% acceptance rate). July 2007

Ensemble Learning in Linearly Combined Classifiers via Negative Correlation [PDF] [GoogleScholar]
Manuela Zanda and Gavin Brown and Giorgio Fumera and Fabio Roli
International Workshop on Multiple Classifier Systems. Prague, May 2007

Intelligent Selection of Application-Specific Garbage Collectors [PDF] [GoogleScholar]
Jeremy Singer, Gavin Brown, Ian Watson, John Cavasos
International Symposium on Memory Management. Oct 2007

Towards Intelligent Analysis Techniques for Object Pretenuring [PDF] [GoogleScholar]
Jeremy Singer, Gavin Brown, Mikel Lujan, Ian Watson
Intl Conference on Principles and Practive of Progamming in Java. Sept 2007

Branch Prediction with Bayesian Networks [PDF] [GoogleScholar]
Jeremy Singer and Gavin Brown and Ian Watson
First Workshop on Statistical and Machine learning approaches applied to ARchitectures and compilaTion (SMART)). January 2007



2006

Journal / Book chapters

Managing Diversity in Regression Ensembles [PDF] [GoogleScholar]
Gavin Brown, Jeremy Wyatt and Peter Tino
Journal of Machine Learning Research. Volume 6, pp 1621-1650 (2006)

Demo code of the algorithm available here


2005

Journals

Diversity Creation Methods: A Survey and Categorisation [PDF] [GoogleScholar]
Gavin Brown, Jeremy Wyatt, Rachel Harris, Xin Yao
Journal of Information Fusion (Special issue on Diversity in Multiple Classifier Systems). Volume 6, issue 1, pp 5-20, March 2005

Conferences

Between Two Extremes: Examining Decompositions of the Ensemble Objective Function [PDF] [GoogleScholar]
Gavin Brown, Jeremy Wyatt and Ping Sun
International Workshop on Multiple Classifier Systems. LNCS, Volume 3541, June 2005



2004

PhD thesis

Diversity in Neural Network Ensembles [PDF] [GoogleScholar]
Gavin Brown
PhD thesis. University of Birmingham 2004 - Winner, BCS Distinguished Dissertation Award 2004

Demo code of the algorithm available here

Patent

Method for Exploiting Ensemble Diversity for Automatic Feature Extraction [PDF] [GoogleScholar]
Gavin Brown, Xin Yao, Heiko Wersing and Bernhard Sendhoff
European Patent no EP1378855. Sponsored by Honda Research Europe.



2003

The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods [PDF] [GoogleScholar]
Gavin Brown and Jeremy Wyatt
International Conference on Machine Learning (ICML'03). Washington DC, USA, August 2003 (32% acceptance)

Negative Correlation Learning and The Ambiguity Family of Ensemble Methods [PDF] [GoogleScholar]
Gavin Brown and Jeremy Wyatt
International Workshop on Multiple Classifier Systems (MCS'03). Washington DC, USA, August 2003



2002

Exploiting Ensemble Diversity for Automatic Feature Extraction [PDF] [GoogleScholar]
Gavin Brown, Xin Yao, Jeremy Wyatt, Heiko Wersing and Bernhard Sendhoff
International Conference on Neural Information Processing (ICONIP'02). Singapore, 2002



2001

On The Effectiveness of Negative Correlation Learning [PDF] [GoogleScholar]
Gavin Brown and Xin Yao
First UK Workshop on Computational Intelligence (UKCI`01). Edinburgh, Scotland, September 2001

Neural Network Ensembles and Their Application to Traffic Flow Prediction in Telecommunications Networks [PDF] [GoogleScholar]
Gavin Brown, Xin Yao and Manfred Fischer
International Joint Conference on Neural Networks (IJCNN'01). IEEE Press, Piscataway, NJ, USA, July 2001