Gavin Brown

Gavin Brown

Professor of Machine Learning

Publications.

You can also find these on my Google Scholar profile.

2024

Bias/Variance is not the same as Approximation/Estimation (PDF)
Gavin Brown and Riccardo Ali
Transactions on Machine Learning Research, 2024

FINESSD: Near-Storage Feature Selection with Mutual Information for Resource-Limited FPGAs (PDF)
Nick Kyparissas, Gavin Brown, Mikel Luján
32nd IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM 2024, 2024

Low-cost and efficient prediction hardware for tabular data using tiny classifier circuits (PDF)
Konstantinos Iordanou, Timothy Atkinson, Emre Ozer, Jedrzej Kufel, Grace Aligada, John Biggs, Gavin Brown & Mikel Luján
Nature Electronics, 2024

2023

A Unified Theory of Diversity in Ensemble Learning (PDF)
Danny Wood, Tingting Mu, Andrew Webb, Henry Reeve, Mikel Lujan, Gavin Brown
Journal of Machine Learning Research, 24(359):1-49, 2023

Malodour classification with low-cost flexible electronics (PDF)
Emre Ozer, Jedrzej Kufel, John Biggs, Anjit Rana, Francisco J. Rodriguez, Thomas Lee-Clark, Antony Sou, Catherine Ramsdale, Scott White, Suresh Kumar Garlapati, Palaniappan Valliappan, Aiman Rahmanudin, Venuskrishnan Komanduri, Glenn Sunley Saez, Sankara Gollu, Gavin Brown, Piotr Dudek, Krishna C. Persaud, Michael L. Turner, Stephanie Murray, Susan Bates, Robert Treloar, Brian Newby, Jane Ford
Nature Communications, vol 14 (777), 2023

2022

A surrogate machine learning model for advanced gas-cooled reactor graphite core safety analysis (PDF)
Huw Rhys Jones, Tingting Mu, Dzifa Kudawoo, Gavin Brown, Philippe Martinuzzi, Neil McLachlan
Journal of Nuclear Engineering and Design, 2022

Predicting domestic abuse (fairly) and police risk assessment (PDF)
Emily Turner, Gavin Brown, Juanjo Medina-Ariza
Journal of Psychosocial Intervention; 31(3): 145–157., 2022

Bias-Variance Decompositions for Margin Losses (PDF)
Danny Wood, Tingting Mu, Gavin Brown
Artificial Intelligence and Statistics (AISTATS), 2022

2021

2020

To ensemble or not ensemble - When does end-to-end training fail (PDF)
AM Webb, C Reynolds, W Chen, H Reeve, DA Iliescu, M Lujan, G Brown
European Conference on Machine Learning, 2020

Binary neural network as a flexible integrated circuit for odour classification (PDF)
Emre Ozer, Jedrzej Kufel, John Biggs, James Myers, Charles Reynolds, Gavin Brown, Anjit Rana, Antony Sou, Catherine Ramsdale, Scott White
IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), 2020

A hardwired machine learning processing engine fabricated with submicron metal-oxide thin-film transistors on a flexible substrate (PDF)
Emre Ozer, Jedrzej Kufel, James Myers, John Biggs, Gavin Brown, Anjit Rana, Antony Sou, Catherine Ramsdale, Scott White
Nature Electronics, Volume 3, Issue 7. Pages 419-425, 2020

Feature selection with limited bit depth mutual information for portable embedded systems (PDF)
Laura Moran-Fernandez, Konstantinos Sechidis, Veronica Bolon-Canedo, Amparo Alonso-Betanzos, Gavin Brown
Journal of Knowledge-Based Systems, Volume 197, 2020

2019

Hybrid extreme learning machine approach for heterogeneous neural networks
Vasileios Christou, Markos G Tsipouras, Nikolaos Giannakeas, Alexandros T Tzallas, Gavin Brown
Journal of Neurocomputing, Volume 361, Pages 137-150, 2019

On the stability of feature selection in the presence of feature correlations (PDF)
Konstantinos Sechidis, Konstantinos Papangelou, Sarah Nogueira, James Weatherall, Gavin Brown
European Conference on Machine Learning, 2019

Bespoke machine learning processor development framework on flexible substrates (PDF)
Emre Ozer, Jedrzej Kufel, John Biggs, Gavin Brown, James Myers, Anjit Rana, Antony Sou, Catherine Ramsdale
IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS), 2019

Insights into distributed feature ranking (PDF)
Veronica Bolon-Canedo, Konstantinos Sechidis, Noelia Sanchez-Marono, Amparo Alonso-Betanzos, Gavin Brown
Information Sciences, 2019

Efficient feature selection using shrinkage estimators (PDF)
K Sechidis, L Azzimonti, A Pocock, G Corani, J Weatherall, G Brown
Machine Learning 108 (8), 1261-1286, 2019

Dashing hopes? The predictive accuracy of domestic abuse risk assessment by police (PDF)
E Turner, J Medina, G Brown
The British Journal of Criminology 59 (5), 1013-1034, 2019

ORB-SLAM-CNN - lessons in adding semantic map construction to feature-based SLAM (PDF)
AM Webb, G Brown, M Luján
Annual Conference Towards Autonomous Robotic Systems, 2019

2018

Distinguishing prognostic and predictive biomarkers - an information theoretic approach (PDF)
K Sechidis, K Papangelou, PD Metcalfe, D Svensson, J Weatherall, G Brown
Bioinformatics 34 (19), 3365-3376, 2018

Modular dimensionality reduction (PDF)
Henry Reeve, Tingting Mu, Gavin Brown
European Conference on Machine Learning (ECML), 2018

Toward an understanding of adversarial examples in clinical trials (PDF)
K Papangelou, K Sechidis, J Weatherall, G Brown
ECML, 2018

Simple strategies for semi-supervised feature selection (PDF)
K Sechidis, G Brown
Machine Learning 107 (2), 357-395, 2018

The k-nearest neighbour ucb algorithm for multi-armed bandits with covariates (PDF)
Henry Reeve, Joseph Mellor, Gavin Brown
Intl Conference on Algorithmic Learning Theory, 2018

Diversity and degrees of freedom in regression ensembles (PDF)
Henry Reeve, Gavin Brown
Neurocomputing vol 298, p55-68, 2018

On the Stability of Feature Selection Algorithms (PDF)
Sarah Nogueira, Kostas Sechidis, Gavin Brown
Journal of Machine Learning Research (JMLR) vol 18, pages 1-54, 2018

2017

Degrees of Freedom in Regression Ensembles (PDF)
Henry W. J. Reeve and Gavin Brown
European Symposium on Artificial Neural Networks (ESANN). Bruges, Belgium.
Best Student Paper Award, 2017

Individual Confidence-Weighting and Group Decision-Making (PDF)
James A.R. Marshall, Gavin Brown, Andrew Radford
Trends in Ecology and Evolution. Volume 2261, 2017

Exploring the consequences of distributed feature selection in DNA microarray data (PDF)
Veronica Bolon-Canedo, Konstantinos Sechidis, Noelia Sanchez-Marono, Amparo Alonso-Betanzos and Gavin Brown
International Joint Conference on Neural Networks (IJCNN). Anchorage, Alaska, USA, 2017

On the Use of Spearman’s Rho to Measure the Stability of Feature Rankings (PDF)
Sarah Nogueira, Konstantinos Sechidis and Gavin Brown
Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), Faro, Portugal, 2017

Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
M. Melis, A. Demontis, B. Biggio, G. Brown, G. Fumera, F. Roli
ICCV Workshop, ViPAR, 2017

Minimax rates on Manifolds with Approximate Nearest Neighbours (PDF)
Henry Reeve and Gavin Brown
International Conference on Algorithmic Learning Theory, Tokyo, 2017. PMLR 76:11-56, 2017

Gradient boosting models for photovoltaic power estimation under partial shading conditions (PDF)
N Nikolaou, E Batzelis, G Brown
International Workshop on Data Analytics for Renewable Energy Integration, 2017

Mutual information for improving the efficiency of the SCH algorithm
D. Fernandez-Francos, O. Fontenla-Romero, A. Alonso-Betanzos and G. Brown
European Symposium on Artificial Neural Networks (ESANN). Bruges, Belgium, 2017

Dealing with Under-reported Variables - An Information Theoretic Solution (PDF)
Konstantinos Sechidis, Matthew Sperrin, Emily S. Petherick, Mikel Lujan, and Gavin Brown
International Journal of Approximate Reasoning. Volume 85, June 2017, Pages 159-177, 2017

Disentangling Prognostic and Predictive Biomarkers Through Mutual Information
Konstantinos Sechidis, Emily Turner, Paul Metcalfe, James Weatherall and Gavin Brown
Informatics for Health. Manchester, UK, 2017

Boosting java performance using gpGPUs (PDF)
James Clarkson, Christos Kotselidis, Gavin Brown, Mikel Luján
International Conference on Architecture of Computing Systems, 2017

2016

Ranking Biomarkers through Mutual Information
Konstantinos Sechidis, Emily Turner, Paul Metcalfe, James Weatherall and Gavin Brown
NIPS Workshop on Machine Learning for Healthcare. Barcelona, December, 2016

Exploring the Relationship Between Eye Movements and Electrocardiogram Interpretation Accuracy (PDF)
Alan Davies, Gavin Brown, Markel Vigo, Simon Harper, Laura Horseman, Bruno Splendiani, Elspeth Hill & Caroline Jay
Nature Scientific reports, 6(1), 2016

Measuring the Stability of Feature Selection (PDF)
Sarah Nogueira and Gavin Brown
European Conference on Machine Learning (ECML). Acceptance rate 99/353 (28%). Italy, Sept, 2016

Note: all results in the ECML paper above have been superseded by our newer 2018 article, published in JMLR.

Estimating Mutual Information in Under-Reported Variables (PDF)
Konstantinos Sechidis, Matthew Sperrin, Emily Petherick, Gavin Brown
Conference on Probabilistic Graphical Models, 2016

Cost-Sensitive Boosting Algorithms - Do we really need them? (PDF)
Nikolas Nikolaou, Meelis Kull, Narayanan Edakunni, Peter Flach, Gavin Brown
Machine Learning Journal, Volume 104, Issue 2, pp 359-384, 2016

Compiler-Driven Software Speculation for Thread-level Parallelism (PDF)
Paraskevas Yiapanis, Gavin Brown, Mikel Lujan
Transactions on Programming Languages and Systems (TOPLAS). Volume 38 Issue 2, January, 2016

2015

53 Ways to Assess Your Students (contributing author - Chapters on Feedback on MCQs and short answer questions, Problem-based assessment, Calculation tasks, and Self-assessment)
Victoria Burns (Editor) Professional and Higher Partnership., 2015

Modular Autoencoders for Ensemble Feature Extraction (PDF)
Henry Reeve and Gavin Brown
NIPS 2015 Workshop on Feature Extraction, Modern Questions and Challenges. Published in Journal of Machine Learning Research W&CP, vol 44, 2015

General Terminology Induction in OWL
Viachaslau Sazonau, Uli Sattler and Gavin Brown
International Semantic Web Conference (ISWC 2015). Research Track - acceptance rate 38/172 (22%). USA, October, 2015

Markov blanket discovery in positive-unlabelled and semi-supervised data (PDF)
Konstantinos Sechidis and Gavin Brown
European Conference on Machine Learning (ECML). Acceptance rate 89/383 (23.2%). Portugal, Sept, 2015

On Unifiers, Diversifiers, and the Nature of Pattern Recognition (PDF)
Gavin Brown
Pattern Recognition Letters (Special Issue on Philosophical Aspects of Pattern Recognition). Volume 64, pages 11-20, 2015

Is Feature Selection Secure against Training Data Poisoning? (PDF)
H.Xiao, B. Biggio , G. Brown, G. Fumera , C. Eckert, F. Roli
International Conference on Machine Learning (ICML). Lille, France, July, 2015

Measuring the Stability of Feature Selection with Applications to Ensemble Methods (PDF)
Sarah Nogueira and Gavin Brown
International Workshop on Multiple Classifier Systems, 2015

Calibrating Adaboost for Asymmetric Learning (PDF)
Nikolaos Nikolaou and Gavin Brown
Intl Workshop on Multiple Classifier Systems. June, 2015

A Scalable Implementation of Information Theoretic Feature Selection for High Dimensional Data
Anthony Kleerekoper, Michael Pappas, Adam Pocock, Gavin Brown, Mikel Lujan
IEEE International Conference on Big Data (IEEE BigData), 2015

Random Ordinality Ensembles - Ensemble methods for multi-valued categorical data
Amir Ahmad and Gavin Brown
Information Sciences. Volume 296, pages 75-94. March, 2015

2014

Statistical Hypothesis Testing in Positive Unlabelled Data (PDF)
Konstantinos Sechidis, Borja Calvo and Gavin Brown
European Conference on Machine Learning (ECML). Acceptance rate 115/484 (23.8%).
Best Student Paper Award. France, Sept, 2014

More info (slides, supplementary material, etc), available here

Structural, Syntactic, and Statistical Pattern Recognition (PDF)
Pasi Fränti, Gavin Brown, Marco Loog, Francisco Escolano, Marcello Pelillo
Edited proceedings: IAPR International Workshop (S+SSPR). Joensuu, Finland, August 20-22, 2014

Random Projection Random Discretization Ensembles – Ensembles of Linear Multivariate Decision Trees (PDF)
Amir Ahmad and Gavin Brown
IEEE Transactions on Knowledge and Data Engineering. Vol 26, Issue 5, pages 143-152, 2014

Information theoretic feature selection in multi-label data through composite likelihood (PDF)
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? (PDF)
Viachaslau Sazonau, Uli Sattler, Gavin Brown
International Conference on Principles of Knowledge Representation and Reasoning (KR 2014), Vienna, Austria, AAAI Press, 2014

2013

Exploring sketches for probability estimation with sublinear memory (PDF)
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)
Michele Filannino, Gavin Brown, and Goran Nenadic
Joint Conference on Lexical and Computational Semantics: Workshop on Semantic Evaluation (SemEval). Atlanta, Georgia, USA. June, 2013

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

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

2012

Informative Priors for Markov Blanket Discovery (PDF)
Adam Pocock, Mikel Lujan, Gavin Brown
Artificial Intelligence and Statistics (AISTATS). La Palma, April, 2012

Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection (PDF)
Gavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luján
Journal of Machine Learning Research, 2012

2011

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

Boosting as a Product of Experts (PDF)
Narayanan Edakunni, Gavin Brown, Tim Kovacs
Uncertainty in Artificial Intelligence (UAI) - acceptance rate 34%. July, 2011

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

Garbage Collection Auto-Tuning for Java MapReduce on Multi-Cores (PDF)
Jeremy Singer, George Kovoor, Gavin Brown, Mikel Lujan
International Symposium on Memory Management. June, 2011

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

From Heuristics to Statistics
Gavin Brown
Keynote at IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments. France, April, 2011

2010

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

Ensemble Learning (PDF)
Gavin Brown
Encyclopaedia of Machine Learning. C.Sammut, G.I.Webb (Eds.) Springer, ISBN 0387307680, 2010

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

Windows Shut on Curiosity (PDF)
Gavin Brown
The Times Higher Education, 3rd June, 2010

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

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

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

Some thoughts at the interface of Ensemble Methods and Feature Selection (PDF)
Gavin Brown
Keynote Lecture, Intl Workshop on Multiple Classifier Systems. Cairo, 2010

Good and Bad Diversity in Majority Vote Ensembles (PDF)
Gavin Brown and Ludmila Kuncheva
Intl Workshop on Multiple Classifier Systems. Cairo, 2010

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

2009

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

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

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

A New Perspective for Information Theoretic Feature Selection (PDF)
Gavin Brown
Artificial Intelligence and Statistics (AISTATS), 2009

An Information Theoretic Perspective on Multiple Classifier Systems (PDF)
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

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

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

2008

An Information Theoretic Evaluation of Software Metrics for Object Lifetime Prediction (PDF)
Sebastien Marion, Gavin Brown, Richard Jones, Mikel Lujan, Chris Ryder and Ian Watson
Workshop on Statistical and Machine learning approaches applied to ARchitectures and compilaTion. January, 2008

2007

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

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

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

UCSpv: Principled Voting in UCS Rule Populations (PDF)
Gavin Brown, Tim Kovacs, James Marshall
Genetic and Evolutionary Computation COnference (GECCO). July, 2007

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

Sparse Distributed Memory using Rank Order Neural Codes (PDF)
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)
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

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

2005

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

Managing diversity in regression ensembles (PDF)
G Brown, JL Wyatt, P Tino
Journal of Machine Learning Research, 2005

Diversity creation methods: a survey and categorisation (PDF)
G Brown, J Wyatt, R Harris, X Yao
Journal of Information Fusion 6 (1), 5-20, 2005

2004

Diversity in Neural Network Ensembles (PDF)
Gavin Brown
PhD thesis, University of Birmingham, 2004

Winner, BCS Distinguished Dissertation Award 2004

Method for Exploiting Ensemble Diversity for Automatic Feature Extraction (PDF)
Xin Yao, Gavin Brown, Bernhard Sendhoff, Heiko Wersing
European Patent no EP1378855. Sponsored by Honda Research Europe, 2004

2003

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

The Use of the Ambiguity Decomposition in Neural Network Ensemble Learning Methods (PDF)
Gavin Brown and Jeremy Wyatt
International Conference on Machine Learning (ICML), 2003

2002

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

2001

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

Neural Network Ensembles and Their Application to Traffic Flow Prediction in Telecommunications Networks (PDF)
Gavin Brown, Xin Yao and Manfred Fischer
International Joint Conference on Neural Networks (IJCNN). USA, 2001