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
Xin YaoSouthern University of Science and Technology and University of Birmingham (UK)Verified email at cs.bham.ac.uk
Henry W J ReeveUniversity of BristolVerified email at bristol.ac.uk
Paraskevas (Paris) YiapanisUniversity of ManchesterVerified email at cs.man.ac.uk
Narayanan U EdakunniUniversity of ManchesterVerified email at cs.man.ac.uk
Fabio RoliProfessor, University of Genova, ItalyVerified email at unige.it
Giorgio FumeraAssociate Professor of Computer Engineering, University of CagliariVerified email at unica.it
Nikolaos NikolaouLecturer in Data Intensive Sciences, UCLVerified email at ucl.ac.uk
Battista BiggioAssociate Professor at University of Cagliari, Co-Founder of Pluribus OneVerified email at unica.it
Tim KovacsUniversity of BristolVerified email at bristol.ac.uk
Ludmila KunchevaProfessor of Computer Science, Bangor University, UKVerified email at bangor.ac.uk
Emre OzerPragmaticVerified email at pragmaticsemi.com
James A. R. MarshallProfessor of Theoretical & Computational Biology, University of SheffieldVerified email at sheffield.ac.uk
Peter TinoProfessor of Complex and Adaptive Systems, University of Birmingham, UKVerified email at cs.bham.ac.uk
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	1362	2012
Diversity creation methods: a survey and categorisation G Brown, J Wyatt, R Harris, X Yao Information Fusion 6 (1), 5-20, 2005	1290	2005
Managing diversity in regression ensembles. G Brown, JL Wyatt, P Tino Journal of machine learning research 6 (9), 2005	476	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	471	2015
Diversity in neural network ensembles G Brown University of Birmingham, 2004	245	2004
A new perspective for information theoretic feature selection G Brown Artificial intelligence and statistics, 49-56, 2009	230	2009
On the Stability of Feature Selection Algorithms S Nogueira, K Sechidis, G Brown Journal of Machine Learning Research 18, 1-54, 2018	203	2018
“Good” and “Bad” diversity in majority vote ensembles G Brown, LI Kuncheva International workshop on multiple classifier systems, 124-133, 2010	169	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	112	2017
Measuring the stability of feature selection S Nogueira, G Brown Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016	97	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	93	2010
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	88	2018
An information theoretic perspective on multiple classifier systems G Brown International Workshop on Multiple Classifier Systems, 344-353, 2009	85	2009
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	80	2013
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	78	2019
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	73	2016
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	64	2007
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	61	2020
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	61	2011
ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge M Filannino, G Brown, G Nenadic arXiv preprint arXiv:1304.7942, 2013	56	2013

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