Gavin Brown
Gavin Brown
Professor of Machine Learning, Dept of Computer Science, University of Manchester
Verified email at - Homepage
Cited by
Cited by
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature Selection
G Brown, A Pocock, MJ Zhao, M LujŠn
Journal of Machine Learning Research 13 (1), 27-66, 2012
Diversity creation methods: a survey and categorisation
G Brown, J Wyatt, R Harris, X Yao
Information Fusion 6 (1), 5-20, 2005
Managing diversity in regression ensembles.
G Brown, JL Wyatt, P Tino
Journal of machine learning research 6 (9), 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
Diversity in neural network ensembles
G Brown
University of Birmingham, 2004
A new perspective for information theoretic feature selection
G Brown
Artificial intelligence and statistics, 49-56, 2009
“Good” and “Bad” diversity in majority vote ensembles
G Brown, LI Kuncheva
International workshop on multiple classifier systems, 124-133, 2010
On the Stability of Feature Selection Algorithms
S Nogueira, K Sechidis, G Brown
Journal of Machine Learning Research 18, 1-54, 2018
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
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
Measuring the stability of feature selection
S Nogueira, G Brown
Joint European conference on machine learning and knowledge discovery in†…, 2016
An information theoretic perspective on multiple classifier systems
G Brown
International Workshop on Multiple Classifier Systems, 344-353, 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
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
Cost-sensitive boosting algorithms: Do we really need them?
N Nikolaou, N Edakunni, M Kull, P Flach, G Brown
Machine Learning 104 (2), 359-384, 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
Garbage collection auto-tuning for java mapreduce on multi-cores
J Singer, G Kovoor, G Brown, M LujŠn
ACM SIGPLAN Notices 46 (11), 109-118, 2011
ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge
M Filannino, G Brown, G Nenadic
arXiv preprint arXiv:1304.7942, 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
Fundamental nano-patterns to characterize and classify java methods
J Singer, G Brown, M LujŠn, A Pocock, P Yiapanis
Electronic Notes in Theoretical Computer Science 253 (7), 191-204, 2010
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