Nicola L. C. Talbot

发表

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

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

The evidence framework applied to sparse kernel logistic regression

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

Sparse Bayesian kernel logistic regression

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

Agnostic Learning versus PriorKnowledge intheDesignofKernelMachines

Nicola L. C. Talbot, GavinC . Cawley, 2007 .

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

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

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.

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.

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..

A fast index assignment method for robust vector quantisation of image data

Gavin C. Cawley, Nicola L. C. Talbot, 1997, Proceedings of International Conference on Image Processing.

Using Latex to Write a PhD Thesis

Nicola L. C. Talbot, 2013 .

Generalised Kernel Machines

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

Latex for Complete Novices

Nicola L. C. Talbot, Nicole Talbot, 2012 .

Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation

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

Estimating Predictive Variances with Kernel Ridge Regression

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

Manipulation of prior probabilities in support vector classification

Gavin C. Cawley, Nicola L. C. Talbot, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Optimally regularised kernel Fisher discriminant classification

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

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.

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.

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.

E-cient Cross-Validationof Kernel FisherDiscriminant Classiflers

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

Approximately unbiased estimation of conditional variance in heteroscedastic kernel ridge regression

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

Efficient cross-validation of kernel fisher discriminant classifiers

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

Efficient formation of a basis in a kernel induced feature space

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

Risk assessment for Clostridium botulinum: a network approach

Gary C. Barker, Nicola L. C. Talbot, Michael W. Peck, 2002 .

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..

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

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

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..

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

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

Reduced Rank Kernel Ridge Regression

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

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..

Kernel learning at the first level of inference

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

Improved sparse least-squares support vector machines

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

Optimally regularised kernel Fisher discriminant analysis

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

Optimally regularised kernel Fisher discriminant analysis

Gavin C. Cawley, Kamel Saadi, Nicola L. C. 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 .

Fast index assignment algorithm for vector quantisation over noisy transmission channels

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