G. Cawley

发表

Efficient approximate leave-one-out cross-validation for kernel logistic regression

G. Cawley, N. Talbot, 2008, Machine Learning.

Model Selection for Support Vector Machines via Adaptive Step-Size Tabu Search

G. Cawley, 2001 .

On a Fast, Compact Approximation of the Exponential Function

Gavin C. Cawley, G. Cawley, 2000, Neural Computation.

Fast exact leave-one-out cross-validation of sparse least-squares support vector machines

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2004, Neural Networks.

Design of the 2015 ChaLearn AutoML challenge

Sergio Escalera, Isabelle Guyon, Gavin C. Cawley, 2015, IJCNN.

Improved sparse least-squares support vector machines

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2002, Neurocomputing.

Model Selection: Beyond the Bayesian/Frequentist Divide

Isabelle Guyon, Gavin C. Cawley, Gideon Dror, 2010, J. Mach. Learn. Res..

Causal & non-causal feature selection for ridge regression

G. Cawley, 2008 .

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

Gavin C. Cawley, Mikko Kolehmainen, Ari Karppinen, 2003 .

Predictive Uncertainty in Environmental Modelling 1 Predictive Uncertainty in Environmental Modelling

Gavin C. Cawley, Gareth J. Janacek, G. Cawley, 2007 .

using a Relevance Vector Machine by microarray analysis and promoter classification Arabidopsisglucose- and ABA-regulated transcription networks in

G. Cawley, M. Bevan, Sean Walsh, 2006 .

Establishing glucose- and ABA-regulated transcription networks in Arabidopsis by microarray analysis and promoter classification using a Relevance Vector Machine.

Caroline Smith, Kee Khoon Lee, Gavin Cawley, 2006, Genome research.

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.1318 DOWNSCALING HEAVY PRECIPITATION OVER THE UNITED KINGDOM: A COMPARISON OF DYNAMICAL AND STATISTICAL METHODS AND THEIR FUTURE SCENARIOS

G. Cawley, R. Wilby, C. Goodess, 2006 .

E-cient Leave-One-Out Cross-Validationof KernelFisherDiscriminant Classiflers

G. Cawley, N. Talbot, N. L. C. Talbot, 2005 .

Efficient model selection for kernel logistic regression

G. Cawley, N. Talbot, 2004, ICPR 2004.

Near-videorealistic synthetic talking faces: implementation and evaluation

Gavin C. Cawley, Barry-John Theobald, Iain A. Matthews, 2004, Speech Commun..

Estimating the Costs Associated with Worthwhile Predictions of Poor Air Quality

D. Mandic, G. Cawley, R. Foxall, 2001 .

A rigorous inter-comparison of ground-level ozone predictions

Gavin C. Cawley, Mikko Kolehmainen, Emil Pelikán, 2003 .

A simple trick for constructing Bayesian formulations of sparse kernel learning methods

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2005 .

LSP speech synthesis using backpropagation networks

Gavin C. Cawley, P. D. Noakes, G. Cawley, 1993 .

Reproducing a Subjective Classification Scheme for Atmospheric Circulation Patterns over the United Kingdom Using a Neural Network

G. Cawley, S. Dorling, 1996, ICANN.

GENERALIZATION IN NEURAL SPEECH SYNTHESIS

G. Cawley, 1998 .

Inter-comparison of neural network, statistical and deterministic models for predicting the concentrations of NO2 and PM10 in urban air

Gavin C. Cawley, Mikko Kolehmainen, Ari Karppinen, 2003 .

of the 2015 ChaLeam AutoML Challenge

Isabelle Guyon, Gavin C. Cawley, Hugo Jair Escalante, 2015 .

E-cient Cross-Validationof Kernel FisherDiscriminant Classiflers

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2003 .

Fast exact leave-one-out cross-validation of least-squares Support Vector Machines

Gavin C. Cawley, Kamel Saadi, Nicola L. C. Talbot, 2002, ESANN.

Trends in Neurocomputing at ESANN 2004

Thomas Villmann, Gavin C. Cawley, Jochen J. Steil, 2005, Neurocomputing.

Heteroscedastic regularised kernel regression for prediction of episodes of poor air quality

Gavin C. Cawley, Stephen R. Dorling, Danilo P. Mandic, 2002, The European Symposium on Artificial Neural Networks.

Causal and Non-Causal Feature Selection for Ridge Regression

Gavin C. Cawley, G. Cawley, 2008, WCCI Causation and Prediction Challenge.

CLOP: a Matlab Learning Object Package

Isabelle Guyon, Gavin C. Cawley, Hugo Jair Escalante, 2007 .

An improved vector quantization algorithm for speech transmission over noisy channels

Gavin C. Cawley, G. Cawley, 1996, ICSLP.

Towards a low bandwidth talking face using appearance models

Gavin C. Cawley, Barry-John Theobald, Silko Kruse, 2003, Image Vis. Comput..

STAtistical and Regional dynamical Downscaling of EXtremes for European regions: some results from the STARDEX project

Gavin C. Cawley, András Bárdossy, Christina Anagnostopoulou, 2003 .

The Application of Neural Networks to Phonetic Modelling

Gavin C. Cawley, G. Cawley, 1996 .

Reconstruction of sub-surface archaeological remains from magnetic data using neural computing.

Gavin C. Cawley, David Bescoby, P. N. Chroston, 2003 .

New Issues in Neurocomputing

Gavin C. Cawley, Jochen J. Steil, Fabrice Rossi, 2006, Neurocomputing.

Over-Fitting in Model Selection with Gaussian Process Regression

Rekar O. Mohammed, G. Cawley, 2017, MLDM.

SYNTHESIS USING BACKPROPAGATION NETWORKS

G. Cawley, P. Noakes, 1993 .

On allometric equations for predicting body mass of dinosaurs

G. Cawley, G. Janacek, 2010 .

Reproducing a Subjective Classi cation Scheme for Atmospheric Circulation Patterns over the United Kingdom using a Neural Network

G. Cawley, S. Dorling, 1999 .

A Simple TrickforConstructing Bayesian Formulations of SparseKernelLearningMethods

G. Cawley, N. Talbot, 2005 .

Baseline Methods for Active Learning

Gavin C. Cawley, G. Cawley, 2011, Active Learning and Experimental Design @ AISTATS.

Is rotation forest the best classifier for problems with continuous features? pdf

Gavin C. Cawley, Michael Flynn, James Large, 2018, ArXiv.

On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2010, J. Mach. Learn. Res..

Generalised Kernel Machines

Gavin C. Cawley, Gareth J. Janacek, Nicola L. C. Talbot, 2007, 2007 International Joint Conference on Neural Networks.

Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2007, 2007 International Joint Conference on Neural Networks.

Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2007, J. Mach. Learn. Res..

Model Selection for Kernel Probit Regression

Gavin C. Cawley, G. Cawley, 2007, ESANN.

Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation

Gavin C. Cawley, Mark A. Girolami, Nicola L. C. Talbot, 2006, NIPS.

Predictive Uncertainty in Environmental Modelling

Gareth J. Janacek, Malcolm R. Haylock, Stephen R. Dorling, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs

Gavin C. Cawley, G. Cawley, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Gene Selection in Cancer Classification using Sparse Logistic Regression with Bayesian Regularisation

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2006 .

Enhanced interpretation of magnetic survey data from archaeological sites using artificial neural networks

Gavin C. Cawley, David Bescoby, P. Neil Chroston, 2006 .

Sparse bayesian kernel survival analysis for modeling the growth domain of microbial pathogens

Gavin C. Cawley, Gareth J. Janacek, Nicola L. C. Talbot, 2006, IEEE Transactions on Neural Networks.

Predicting sugar regulation in Arabidopsis thaliana using kernel learning methods

G. Cawley, M. Bevan, K. Lee, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Sparse Bayesian learning and the relevance multi-layer perceptron network

G.C. Cawley, N.L.C. Talbot, G. Cawley, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

A simple trick for constructing Bayesian formulations of sparse kernel learning methods

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

The evidence framework applied to sparse kernel logistic regression

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2005, Neurocomputing.

Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis

Gavin C. Cawley, Gareth J. Janacek, Nicola L. C. Talbot, 2004, Deterministic and Statistical Methods in Machine Learning.

Efficient model selection for kernel logistic regression

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Sparse Bayesian kernel logistic regression

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2004, ESANN.

Heteroscedastic kernel ridge regression

Gavin C. Cawley, Nicola L.C. Talbot, Robert J. Foxall, 2004, Neurocomputing.

Reduced Rank Kernel Ridge Regression

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2002, Neural Processing Letters.

Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2003, Pattern Recognit..

Statistical downscaling with artificial neural networks

Gavin C. Cawley, Malcolm R. Haylock, Stephen R. Dorling, 2003, ESANN.

E cient leave-one-out cross-validation of kernel Fisher discriminant classi ' ers

G. Cawley, N. Talbot, N. L. C. Talbot, 2003 .

A Greedy Training Algorithm for Sparse Least-Squares Support Vector Machines

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2002, ICANN.

Statistical models to assess the health effects and to forecast ground-level ozone

Gavin C. Cawley, Stephen R. Dorling, Emil Pelikán, 2006, Environ. Model. Softw..

Nested cross-validation when selecting classifiers is overzealous for most practical applications pdf

Jacques Wainer, Gavin C. Cawley, G. Cawley, 2018, Expert Syst. Appl..

Fast index assignment algorithm for vector quantisation over noisy transmission channels

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 1996 .

Relevance Vector Machine by microarray analysis and promoter classificationArabidopsis Establishing glucose-and ABA-regulated transcription networks in data

G. Cawley, M. Bevan, Sean Walsh, 2006 .

Benchmarking the semi-supervised naïve Bayes classifier

Gavin C. Cawley, Anthony J. Bagnall, Awat A. Saeed, 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

Results of the Active Learning Challenge

Isabelle Guyon, Gavin C. Cawley, Vincent Lemaire, 2011, Active Learning and Experimental Design @ AISTATS.

Empirical Evaluation of Resampling Procedures for Optimising SVM Hyperparameters

Jacques Wainer, Gavin C. Cawley, G. Cawley, 2017, J. Mach. Learn. Res..

Machine learning competition in immunology - Prediction of HLA class I binding peptides.

Harris Papadopoulos, Tomer Hertz, Vladimir Brusic, 2011, Journal of immunological methods.

Speech coding and synthesis using parametric curves

Gavin C. Cawley, Luis M. T. Jesus, Luis Miguel Teixeira de Jesus, 1997, EUROSPEECH.

Quantitative method for the assignment of hinge and shear mechanism in protein domain movements

Gavin C. Cawley, Steven Hayward, Daniel Taylor, 2014, Bioinform..

Estimating Predictive Variances with Kernel Ridge Regression

Gavin C. Cawley, Nicola L. C. Talbot, Olivier Chapelle, 2005, MLCW.

Approximately unbiased estimation of conditional variance in heteroscedastic kernel ridge regression

Gavin C. Cawley, Stephen R. Dorling, Danilo P. Mandic, 2003, ESANN.

Error Functions for Prediction of Episodes of Poor Air Quality

Gavin C. Cawley, Stephen R. Dorling, Danilo P. Mandic, 2002, ICANN.

Towards video realistic synthetic visual speech

Gavin C. Cawley, Barry-John Theobald, Iain A. Matthews, 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Simplified implementation of the recursive median sieve

Gavin C. Cawley, Pertti Koivisto, Ilya Shmulevich, 2001, Signal Process..

On the Atmospheric Residence Time of Anthropogenically Sourced Carbon Dioxide

G. Cawley, 2011 .

The Temporal Dictionary Ensemble (TDE) Classifier for Time Series Classification

Gavin C. Cawley, James Large, Anthony J. Bagnall, 2020, ECML/PKDD.

Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge

Isabelle Guyon, Gavin C. Cawley, Gideon Dror, 2008, Neural Networks.

On a minimal model for estimating climate sensitivity

G. Cawley, P. Jacobs, K. Cowtan, 2015 .

Efficient formation of a basis in a kernel induced feature space

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2002, ESANN.

Modelling SO2 concentration at a point with statistical approaches

Gavin C. Cawley, Stephen R. Dorling, Giuseppe Nunnari, 2004, Environ. Model. Softw..

Over-Fitting in Model Selection and Its Avoidance

Gavin C. Cawley, G. Cawley, 2012, IDA.

Machine Learning Competition in Immunology – Prediction of HLA class I molecules

Christos, M. Polycarpou, O. Lund, 2011 .

Gene selection in cancer classification using sparse logistic regression with Bayesian regularization

G. Cawley, N. Talbot, 2006, Bioinform..

Optimally regularised kernel Fisher discriminant classification

Gavin C. Cawley, Kamel Saadi, Nicola L. C. Talbot, 2007, Neural Networks.

Optimally regularised kernel Fisher discriminant analysis

G. Cawley, N. L. C. Talbot, Kamel Saadi, 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

Efficient cross-validation of kernel fisher discriminant classifiers

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2003, ESANN.

Non-retrieval: Blocking Pornographic Images

Gavin C. Cawley, Yi Chan, Richard Harvey, 2002, CIVR.

EÆcient Sequential Minimal Optimisation of Support Vector Classi ers

G. Cawley, 2001 .

Kernel learning at the first level of inference

Gavin C. Cawley, Nicola L. C. Talbot, G. Cawley, 2014, Neural Networks.

Agnostic Learning vs. Prior Knowledge Challenge

G. Dror, G. Cawley, Amir Saffari, 2007, 2007 International Joint Conference on Neural Networks.

Investigation of sequence features of hinge-bending regions in proteins with domain movements using kernel logistic regression

S. Hayward, G. Cawley, Ruth Veevers, 2020, BMC Bioinformatics.

Classification of Domain Movements in Proteins Using Dynamic Contact Graphs

Steven Hayward, Gavin Cawley, Daniel Taylor, 2013, PloS one.

Predictive uncertainty in environmental modelling

G. Cawley, G. Janacek, S. Dorling, 2006, Neural Networks.

Predictive Uncertainty in Environmental Modelling

G. Cawley, M. R. Haylock, Stephen R. Dorling, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Interpretation of geophysical surveys of archaeological sites using artificial neural networks

Gavin C. Cawley, P. N. Chroston, D. J. Bescoby, 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

2.5D Visual Speech Synthesis Using Appearance Models

Gavin C. Cawley, Barry-John Theobald, John R. W. Glauert, 2003, BMVC.

Design and analysis of the WCCI 2010 active learning challenge

Isabelle Guyon, Gavin C. Cawley, Vincent Lemaire, 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

Analysis of the IJCNN 2007 agnostic learning vs. prior knowledge challenge

G. Dror, G. Cawley, Amir Saffari, 2008, Neural Networks.

On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms pdf

Gavin C. Cawley, Anthony J. Bagnall, G. Cawley, 2017, ArXiv.