Gavin Brown 个人学术档案 - 学术资源搜索

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Mikel LujanDepartment of Computer Science, University of Manchester在 manchester.ac.uk 的电子邮件经过验证
Konstantinos SechidisNovartis在 novartis.com 的电子邮件经过验证
Adam PocockResearcher at Oracle Labs Machine Learning Research Group在 oracle.com 的电子邮件经过验证
Jeremy L WyattDirector, Applied Science, Amazon在 amazon.com 的电子邮件经过验证
Sarah NogueiraPhD student in Computer Science, University of Manchester在 manchester.ac.uk 的电子邮件经过验证
Henry W J ReeveUniversity of Bristol在 bristol.ac.uk 的电子邮件经过验证
Fabio RoliProfessor, University of Genova, Italy在 unige.it 的电子邮件经过验证
Giorgio FumeraAssociate Professor of Computer Engineering, University of Cagliari在 unica.it 的电子邮件经过验证
Narayanan U EdakunniUniversity of Manchester在 cs.man.ac.uk 的电子邮件经过验证
Paraskevas (Paris) YiapanisUniversity of Manchester在 cs.man.ac.uk 的电子邮件经过验证
Nikolaos NikolaouLecturer in Data Intensive Sciences, UCL在 ucl.ac.uk 的电子邮件经过验证
Battista BiggioFull Professor, University of Cagliari, Italy在 unica.it 的电子邮件经过验证
Emre OzerPragmatic在 pragmaticsemi.com 的电子邮件经过验证
John BiggsDistinguished Fellow Pragmatic and ARM Co-Founder在 duboulaybiggs.com 的电子邮件经过验证
Tim KovacsUniversity of Bristol在 bristol.ac.uk 的电子邮件经过验证
Ludmila KunchevaProfessor of Computer Science, Bangor University, UK在 bangor.ac.uk 的电子邮件经过验证
James A. R. MarshallProfessor of Theoretical & Computational Biology, University of Sheffield在 sheffield.ac.uk 的电子邮件经过验证
Huang XiaoHSBC在 hsbc.com 的电子邮件经过验证
Peter TinoProfessor of Complex and Adaptive Systems, University of Birmingham, UK在 cs.bham.ac.uk 的电子邮件经过验证
James Myersimec在 imec.be 的电子邮件经过验证

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Gavin Brown

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

在 manchester.ac.uk 的电子邮件经过验证 - 首页

Machine Learning Artificial Intelligence Pattern Recognition


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	1417	2012
Diversity creation methods: a survey and categorisation G Brown, J Wyatt, R Harris, X Yao Information Fusion 6 (1), 5-20, 2005	1322	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	505	2015
Managing diversity in regression ensembles. G Brown, JL Wyatt, P Tino Journal of machine learning research 6 (9), 2005	494	2005
Diversity in neural network ensembles G Brown University of Birmingham, 2004	267	2004
A new perspective for information theoretic feature selection G Brown Artificial intelligence and statistics, 49-56, 2009	243	2009
On the Stability of Feature Selection Algorithms S Nogueira, K Sechidis, G Brown Journal of Machine Learning Research 18, 1-54, 2018	229	2018
“Good” and “Bad” diversity in majority vote ensembles G Brown, LI Kuncheva International workshop on multiple classifier systems, 124-133, 2010	171	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	114	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	102	2018
Measuring the stability of feature selection S Nogueira, G Brown Machine Learning and Knowledge Discovery in Databases: European Conference …, 2016	101	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	98	2010
An information theoretic perspective on multiple classifier systems G Brown International Workshop on Multiple Classifier Systems, 344-353, 2009	88	2009
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	87	2019
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
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	78	2016
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	74	2020
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	65	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	62	2011
ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge M Filannino, G Brown, G Nenadic Second Joint Conference on Lexical and Computational Semantics (* SEM …, 2013	56	2013

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