Gavin Brown

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Mikel LujanDepartment of Computer Science, University of ManchesterVerified email at manchester.ac.uk
Konstantinos SechidisNovartisVerified email at novartis.com
Adam PocockResearcher at Oracle Labs Machine Learning Research GroupVerified email at oracle.com
Jeremy L WyattDirector, Applied Science, AmazonVerified email at amazon.com
Sarah NogueiraPhD student in Computer Science, University of ManchesterVerified email at manchester.ac.uk
Henry W J ReeveUniversity of BristolVerified email at bristol.ac.uk
Fabio RoliProfessor, University of Genova and Cagliari, ItalyVerified email at unige.it
Giorgio FumeraAssociate Professor of Computer Engineering, University of CagliariVerified email at unica.it
Narayanan U EdakunniUniversity of ManchesterVerified email at cs.man.ac.uk
Paraskevas (Paris) YiapanisUniversity of ManchesterVerified email at cs.man.ac.uk
Nikolaos NikolaouLecturer in Data Intensive Sciences, UCLVerified email at ucl.ac.uk
Battista BiggioFull Professor, University of Cagliari, ItalyVerified email at unica.it
Emre OzerPragmaticVerified email at pragmaticsemi.com
John BiggsDistinguished Fellow Pragmatic and ARM Co-FounderVerified email at duboulaybiggs.com
Tim KovacsUniversity of BristolVerified email at bristol.ac.uk
Ludmila KunchevaProfessor of Computer Science, Bangor University, UKVerified email at bangor.ac.uk
James A. R. MarshallProfessor of Theoretical & Computational Biology, University of SheffieldVerified email at sheffield.ac.uk
Andrew M. Webbprev. University of ManchesterVerified email at awebb.info
Huang XiaoHSBCVerified email at hsbc.com
James MyersimecVerified email at imec.be

Gavin Brown

Professor of Machine Learning, Dept of Computer Science, University of Manchester

Verified email at manchester.ac.uk - Homepage

Machine Learning Artificial Intelligence Pattern Recognition


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Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection G Brown, A Pocock, MJ Zhao, M Luján Journal of Machine Learning Research 13 (1), 27-66, 2012	1464	2012
Diversity creation methods: a survey and categorisation G Brown, J Wyatt, R Harris, X Yao Information Fusion 6 (1), 5-20, 2005	1348	2005
Is feature selection secure against training data poisoning? H Xiao, B Biggio, G Brown, G Fumera, C Eckert, F Roli international conference on machine learning, 1689-1698, 2015	530	2015
Managing diversity in regression ensembles. G Brown, JL Wyatt, P Tino Journal of machine learning research 6 (9), 2005	506	2005
Diversity in neural network ensembles G Brown University of Birmingham, 2004	271	2004
On the Stability of Feature Selection Algorithms S Nogueira, K Sechidis, G Brown Journal of Machine Learning Research 18, 1-54, 2018	252	2018
A new perspective for information theoretic feature selection G Brown Artificial intelligence and statistics, 49-56, 2009	247	2009
“Good” and “Bad” diversity in majority vote ensembles G Brown, LI Kuncheva International workshop on multiple classifier systems, 124-133, 2010	173	2010
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 Proceedings of the IEEE international conference on computer vision …, 2017	122	2017
Distinguishing prognostic and predictive biomarkers: an information theoretic approach K Sechidis, K Papangelou, PD Metcalfe, D Svensson, J Weatherall, ... Bioinformatics 34 (19), 3365-3376, 2018	113	2018
Measuring the stability of feature selection S Nogueira, G Brown Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016	107	2016
Learn++. MF: A random subspace approach for the missing feature problem R Polikar, J DePasquale, HS Mohammed, G Brown, LI Kuncheva Pattern Recognition 43 (11), 3817-3832, 2010	102	2010
Dashing hopes? The predictive accuracy of domestic abuse risk assessment by police E Turner, J Medina, G Brown The British journal of criminology 59 (5), 1013-1034, 2019	96	2019
A hardwired machine learning processing engine fabricated with submicron metal-oxide thin-film transistors on a flexible substrate E Ozer, J Kufel, J Myers, J Biggs, G Brown, A Rana, A Sou, C Ramsdale, ... Nature Electronics 3 (7), 419-425, 2020	93	2020
An information theoretic perspective on multiple classifier systems G Brown International Workshop on Multiple Classifier Systems, 344-353, 2009	89	2009
Cost-sensitive boosting algorithms: Do we really need them? N Nikolaou, N Edakunni, M Kull, P Flach, G Brown Machine Learning 104, 359-384, 2016	82	2016
Beyond Fano's inequality: Bounds on the optimal F-score, BER, and cost-sensitive risk and their implications MJ Zhao, N Edakunni, A Pocock, G Brown The Journal of Machine Learning Research 14 (1), 1033-1090, 2013	81	2013
Intelligent selection of application-specific garbage collectors J Singer, G Brown, I Watson, J Cavazos Proceedings of the 6th international symposium on Memory management, 91-102, 2007	66	2007
Garbage collection auto-tuning for java mapreduce on multi-cores J Singer, G Kovoor, G Brown, M Luján ACM SIGPLAN Notices 46 (11), 109-118, 2011	63	2011
Individual confidence-weighting and group decision-making JAR Marshall, G Brown, AN Radford Trends in ecology & evolution 32 (9), 636-645, 2017	61	2017

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