Gavin C Cawley

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Nicola TalbotUniversity of East AngliaVerified email at cmp.uea.ac.uk
S.R. DorlingUniversity of East AngliaVerified email at uea.ac.uk
Gideon DrorProfessor of Computer Science, Academic College of Tel AvivVerified email at mta.ac.il
Mikko KolehmainenProfessor of environmental informatics and modelling, University of Eastern FinlandVerified email at uef.fi
Tony BagnallProfessor of Computer Science, University of SouthamptonVerified email at uea.ac.uk
Amir SaffariAmazon AGI, London UKVerified email at ymer.org
Tim ChattertonIndependent Research and ConsultancyVerified email at racfoundation.org
Danilo MandicProf. of Machine Intelligence, Dept of Electrical and Electronic Eng., Imperial College London, UKVerified email at imperial.ac.uk
Ari KarppinenDocent, Head of Research, Finnish Meteorological InstituteVerified email at fmi.fi
Barry-John TheobaldApple Inc.Verified email at apple.com
Jaakko KukkonenProf, Finnish Meteorological Institute, Docent, Univ. Helsinki, Visiting Prof., Univ. HertfordshireVerified email at fmi.fi
gareth janacekUeaVerified email at uea.ac.uk
Iain MatthewsEpic Games / University of East Anglia / Carnegie Mellon UniversityVerified email at uea.ac.uk
Heikki JunninenUniversity of Tartu, Institute of PhysicsVerified email at ut.ee
Hugo Jair EscalanteINAOEVerified email at inaoep.mx
Richard HarveyProfessor of Computer Science, University of East AngliaVerified email at uea.ac.uk
clare goodessClimatic Research Unit, University of East AngliaVerified email at uea.ac.uk
Steven HaywardSchool of Computing Sciences, University of East AngliaVerified email at cmp.uea.ac.uk
Jacques WainerUniversity of CampinasVerified email at ic.unicamp.br
Karim SorefanUniversity of SheffieldVerified email at sheffield.ac.uk

Gavin C Cawley

Professor of Computing Sciences, University of East Anglia

Verified email at uea.ac.uk - Homepage

Machine Learning


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On over-fitting in model selection and subsequent selection bias in performance evaluation GC Cawley, NLC Talbot The Journal of Machine Learning Research 11, 2079-2107, 2010	2760	2010
Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers GC Cawley, NLC Talbot Pattern recognition 36 (11), 2585-2592, 2003	559	2003
Downscaling heavy precipitation over the United Kingdom: a comparison of dynamical and statistical methods and their future scenarios MR Haylock, GC Cawley, C Harpham, RL Wilby, CM Goodess International Journal of Climatology: A Journal of the Royal Meteorological …, 2006	460	2006
Fast exact leave-one-out cross-validation of sparse least-squares support vector machines GC Cawley, NLC Talbot Neural networks 17 (10), 1467-1475, 2004	425	2004
Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki J Kukkonen, L Partanen, A Karppinen, J Ruuskanen, H Junninen, ... Atmospheric Environment 37 (32), 4539-4550, 2003	415	2003
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters. GC Cawley, NLC Talbot Journal of Machine Learning Research 8 (4), 2007	380	2007
Establishing glucose-and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine Y Li, KK Lee, S Walsh, C Smith, S Hadingham, K Sorefan, G Cawley, ... Genome research 16 (3), 414-427, 2006	316	2006
Gene selection in cancer classification using sparse logistic regression with Bayesian regularization GC Cawley, NLC Talbot Bioinformatics 22 (19), 2348-2355, 2006	303	2006
Sparse multinomial logistic regression via bayesian l1 regularisation G Cawley, N Talbot, M Girolami Advances in neural information processing systems 19, 2006	299	2006
Leave-one-out cross-validation based model selection criteria for weighted LS-SVMs GC Cawley The 2006 IEEE international joint conference on neural network proceedings …, 2006	286	2006
Nested cross-validation when selecting classifiers is overzealous for most practical applications J Wainer, G Cawley Expert Systems with Applications 182, 115222, 2021	257	2021
Model selection: Beyond the Bayesian/frequentist divide I Guyon, A Saffari, G Dror, G Cawley Journal of Machine Learning Research 11 (Jan), 61-87, 2010	231	2010
A rigorous inter-comparison of ground-level ozone predictions U Schlink, S Dorling, E Pelikan, G Nunnari, G Cawley, H Junninen, ... Atmospheric Environment 37 (23), 3237-3253, 2003	178	2003
Statistical models to assess the health effects and to forecast ground-level ozone U Schlink, O Herbarth, M Richter, S Dorling, G Nunnari, G Cawley, ... Environmental Modelling & Software 21 (4), 547-558, 2006	166	2006
Design of the 2015 chalearn automl challenge I Guyon, K Bennett, G Cawley, HJ Escalante, S Escalera, TK Ho, N Macià, ... 2015 International joint conference on neural networks (IJCNN), 1-8, 2015	164	2015
Modelling SO2 concentration at a point with statistical approaches G Nunnari, S Dorling, U Schlink, G Cawley, R Foxall, T Chatterton Environmental Modelling & Software 19 (10), 887-905, 2004	144	2004
Non-retrieval: blocking pornographic images A Bosson, GC Cawley, Y Chan, R Harvey International Conference on Image and Video Retrieval, 50-60, 2002	132	2002
Results of the active learning challenge I Guyon, GC Cawley, G Dror, V Lemaire Active Learning and Experimental Design workshop In conjunction with AISTATS …, 2011	120	2011
Improved sparse least-squares support vector machines GC Cawley, NLC Talbot Neurocomputing 48 (1-4), 1025-1031, 2002	112	2002
The temporal dictionary ensemble (TDE) classifier for time series classification M Middlehurst, J Large, G Cawley, A Bagnall Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021	107	2021

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