N. Talbot

发表

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

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

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.

Improved sparse least-squares support vector machines

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

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.

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

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

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.

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.

A Simple TrickforConstructing Bayesian Formulations of SparseKernelLearningMethods

G. Cawley, N. Talbot, 2005 .

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..

Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation

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

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

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

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..

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.

Fast index assignment algorithm for vector quantisation over noisy transmission channels

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

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.

Efficient formation of a basis in a kernel induced feature space

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

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

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