Publications
Found 66 items. Listing oldest to newest.
Talbot, NLC and Massara, RE. An application oriented comparison of optimization and neural network based design techniques, in 36th Mid-West Symposium on Circuits and Systems. Aug 1993. pp. 261–264.
@inproceedings{Talbot1993,
author = {Nicola L. C. Talbot and Rob E. Massara},
title = {An application oriented comparison of optimization and neural network based design techniques},
booktitle = {36th Mid-West Symposium on Circuits and Systems},
year = 1993,
month = AUG,
pages = {261--264},
doi = {10.1109/MWSCAS.1993.343080}
}
Nicola L. C. Talbot. An application-oriented comparison of optimisation and neural-based design techniques, Mar 1996. Department of Electronic Systems Engineering, University of Essex.
@phdthesis{Talbot1996b,
author = {Nicola L. C. Talbot},
title = {An application-oriented comparison of optimisation and neural-based design techniques},
year = 1996,
month = MAR,
school = {Department of Electronic Systems Engineering, University of Essex}
}
Cawley, GC and Talbot, NLC. A Fast Index Assignment Algorithm for Vector Quantization over Noisy Transmission Channels, Electronics Letters. Jul 1996. Vol. 32, pp. 1343–1344.
@article{Cawley1996b,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {A Fast Index Assignment Algorithm for Vector Quantization over Noisy Transmission Channels},
journal = {Electronics Letters},
year = 1996,
month = JUL,
volume = {32},
pages = {1343--1344},
issn = {0013-5914}
}
Barker, GC, Talbot NLC and Peck, MW. Risk assessment for microbial contamination hazards: a network approach, Nov 1996. Poster presentation and abstract at MAFF Hygienic Food Processing Workshop for Engineers and Microbiologists.
@misc{Barker1996,
author = {Gary C. Barker and Nicola L. C. Talbot and Mike W. Peck},
title = {Risk assessment for microbial contamination hazards: a network approach},
year = 1996,
month = NOV,
address = {London, UK},
note = {Poster presentation and abstract at MAFF Hygienic Food Processing Workshop for Engineers and Microbiologists}
}
Talbot, NLC and Cawley, GC. A Quadratic Index Assignment Algorithm for Vector Quantisation over Noisy Transmission Channels, in Proceedings of the Institute of Acoustics Autumn Conference (Speech and Hearing 96). Nov 1996. Part 9. Vol. 18, pp. 195–199.
@inproceedings{Talbot1996,
author = {Talbot, Nicola L. C. and Cawley, Gavin C.},
title = {A Quadratic Index Assignment Algorithm for Vector Quantisation over Noisy Transmission Channels},
booktitle = {Proceedings of the Institute of Acoustics Autumn Conference (Speech and Hearing 96)},
year = 1996,
month = NOV,
part = {9},
volume = {18},
pages = {195--199}
}
Talbot, NLC and Massara, RE. A quadratic assignment algorithm that takes module size into account, IEE Electronic Letters. 1997. Vol. 33(14).
@article{Talbot1997b,
author = {Nicola L. C. Talbot and Rob E. Massara},
title = {A quadratic assignment algorithm that takes module size into account},
journal = {IEE Electronic Letters},
year = 1997,
volume = {33},
number = {14},
doi = {10.1049/el:19970823}
}
Barker, GC, Talbot NLC and Peck, MW. Microbial risk assessment: a network approach, Jan 1997. Poster presentation and abstract at Department of Health/ACDP Seminar on Microbial Risk Assessment.
@misc{Barker1997b,
author = {Gary C. Barker and Nicola L. C. Talbot and Mike W. Peck},
title = {Microbial risk assessment: a network approach},
year = 1997,
month = JAN,
address = {London, UK},
note = {Poster presentation and abstract at Department of Health/ACDP Seminar on Microbial Risk Assessment}
}
Barker, GC, Talbot NLC and Peck, MW. Mathematical aspects of quantitative risk assessment, Mar 1997. Invited oral presentation at BBSRC Food Directorate Microbiology Workshop.
@misc{Barker1997a,
author = {Gary C. Barker and Nicola L. C. Talbot and Mike W. Peck},
title = {Mathematical aspects of quantitative risk assessment},
year = 1997,
month = MAR,
address = {Birmingham, UK},
note = {Invited oral presentation at BBSRC Food Directorate Microbiology Workshop}
}
Talbot, NLC and Cawley, GC. A Fast Index Assignment Method for Robust Vector Quantisation of Image Data, in Proceedings of the IEEE International Conference on Image Processing (ICIP-97). 26-29 Oct 1997. Vol. 3, pp. 674–677.
@inproceedings{Talbot1997,
author = {Talbot, Nicola L. C. and Cawley, Gavin C.},
title = {A Fast Index Assignment Method for Robust Vector Quantisation of Image Data},
booktitle = {Proceedings of the IEEE International Conference on Image Processing (ICIP-97)},
year = 1997,
month = OCT,
volume = {3},
pages = {674--677},
address = {Santa Barbara, California, U.S.A.},
isbn = {0-8186-8183-7}
}
Peck, MW, Talbot, NLC, and Barker, GC. Quantitative risk assessment for Clostridium botulinum in minimally processed foods, Nov 1998. Oral presentation and abstract at IBRCC (Interagency botulism research co-ordinating committee) meeting.
@misc{Peck1998,
author = {Mike W. Peck and Nicola L. C. Talbot and Gary C. Barker},
title = {Quantitative risk assessment for \emph{Clostridium botulinum} in minimally processed foods},
year = 1998,
month = NOV,
address = {Fort Washington, Pennsylvannia, USA},
note = {Oral presentation and abstract at IBRCC (Interagency botulism research co-ordinating committee) meeting}
}
Peck, MW, Talbot, NLC, and Barker, GC. Risk assessment for spore-forming bacteria in food: Bayesian belief representations, in Food Microbiology and Food Safety into the next millennium. 1999. Foundation Food Micro '99: A.J.Zeist. pp. 442–443.
@inproceedings{Peck1999,
author = {Mike W. Peck and Nicola L. C. Talbot and Gary C. Barker},
title = {Risk assessment for spore-forming bacteria in food: {B}ayesian belief representations},
booktitle = {Food Microbiology and Food Safety into the next millennium},
editor = {A. C. J. Tuitelaars and R. A. Samson and F. M. Rombouts and S. Notermans},
year = 1999,
pages = {442--443},
publisher = {Foundation Food Micro '99: A.J.Zeist},
address = {The Netherlands}
}
Barker, GC, Talbot NLC and Peck, MW. Microbial risk assessment for sous-vide foods, Third European Symposium on Sous-Vide. Mar 1999.
@article{Barker1999a,
author = {Gary C. Barker and Nicola L. C. Talbot and Mike W. Peck},
title = {Microbial risk assessment for sous-vide foods},
journal = {Third European Symposium on Sous-Vide},
year = 1999,
month = MAR,
address = {Leuvan}
}
Cawley, GC and Talbot, NLC. Manipulation of prior probabilities in support vector classification, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2001). 15-19 Jul 2001. pp. 2433–2438.
@inproceedings{Cawley2001d,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Manipulation of prior probabilities in support vector classification},
booktitle = {Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2001)},
year = 2001,
month = JUL,
pages = {2433--2438},
address = {Washington, D.C., U.S.A.},
abstract = {Asymmetric margin error costs for positive and negative examples are
often cited as an efficient heuristic compensating for unrepresentative
priors in training support vector classifiers. In this paper we show
that this heuristic is well justified via simple resampling ideas
applied to the dual Lagrangian defining the 1-norm soft-margin support
vector machine. This observation also provides a simple expression
for the asymptotically optimal ratio of margin error penalties, eliminating
the need for the trial-and-error experimentation normally encountered.
This method allows the use of a smaller, balanced training data set
in problems characterised by widely disparate prior probabilities,
reducing training time. We demonstrate the usefulness of this method
on a real world benchmark problem, that of predicting forest cover
type given only cartographic data}
}
Barker, GC, Talbot NLC and Peck, MW. Risk assessment for Clostridium botulinum: a network approach, International Journal of Biodeterioration and Biodegredation. 2002. Vol. 50, pp. 167–175.
@article{Barker2002,
author = {Gary C. Barker and Nicola L. C. Talbot and Michael W. Peck},
title = {Risk assessment for \emph{Clostridium botulinum}: a network approach},
journal = {International Journal of Biodeterioration and Biodegredation},
year = 2002,
volume = {50},
pages = {167--175},
doi = {10.1016/S0964-8305(02)00083-5},
abstract = {The construction and implementation of a mathematical framework for
the representation of the hazards that arise from \emph{Clostridium
botulinum} growth, and toxin production, in food are described. Botulism
has been recognised as a serious foodborne illness for over a century
and, more recently, has become the subject of increased concern due
to changing processing and consumption patterns associated with foods.
In this respect quantitative risk assessment has an increasingly
important role to play in assisting risk management and ensuring
the safety of minimally processed foods and foods with extended shelf
life.\par Bayesian Belief Networks are a type of expert system
that integrates a graphical flow diagram like, representation of
a hazard domain with a powerful technique for combining probabilities.
This technique facilitates the accumulation of understanding and
experience, for particular hazard domains, into computer tools that
can be used to inspect risks and account for decisions.\par Analysis
of the hazards associated with foodborne botulism involves Belief
Network components that represent contamination processes, thermal
death kinetics for spores, germination and growth of cells, toxin
production and patterns of consumer behaviour, etc. These developments
are discussed and three important aspects of the food safety information
supply, complexity, dependency and uncertainty highlighted. The benefits
associated with a Bayesian view of food safety assessment are illustrated
by a Belief Network representation which supports, and prioritises,
decisions and actions that (a) minimise the chances and extent of
the detrimental events and (b) maximise opportunities for awareness
and control.}
}
Cawley, GC and Talbot, NLC. Efficient formation of a basis in a kernel induced feature space, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002). 24-26 Apr 2002. pp. 1–6.
@inproceedings{Cawley2002a,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Efficient formation of a basis in a kernel induced feature space},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002)},
year = 2002,
month = APR,
pages = {1--6},
address = {Bruges, Belgium},
abstract = {Baudat and Anouar (2001) propose a simple greedy algorithm for estimation
of an approximate basis of the subspace spanned by a set of fixed
vectors embedded in a kernel induced feature space. The resulting
set of basis vectors can then be used to construct sparse kernel
expansions for classification and regression tasks. In this paper
we describe five algorithmic improvements to the method of Baudat
and Anouar, allowing the construction of an approximate basis with
a computational complexity that is independent of the number of training
patterns, depending only on the number of basis vectors extracted}
}
Foxall, RJ, Cawley, GC, Talbot, NLC, Dorling, SR, and Mandic, DP. Heteroscedastic regularised kernel regression for prediction of episodes of poor air quality, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002). 24-26 Apr 2002. pp. 19–24.
@inproceedings{Foxall2002a,
author = {Foxall, Robert J. and Cawley, Gavin C. and Talbot, Nicola L. C. and Dorling, Stephen R. and Mandic, Danilo P.},
title = {Heteroscedastic regularised kernel regression for prediction of episodes of poor air quality},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002)},
year = 2002,
month = APR,
pages = {19--24},
address = {Bruges, Belgium},
abstract = {A regularised kernel regression model is introduced for data characterised
by a heteroscedastic (input dependent variance) Gaussian noise process.
The proposed model provides more robust estimates of the conditional
mean than standard models and also confidence intervals (error bars)
on predictions. The benefits of the proposed model are demonstrated
for the task of non-linear prediction of episodes of poor air quality
in urban environments}
}
Saadi, K, Cawley, GC, and Talbot, NLC. Fast exact leave-one-out cross-validation of least-squares support vector machines, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002). 24-26 Apr 2002. pp. 149–154.
@inproceedings{Saadi2002,
author = {Saadi, K. and Cawley, Gavin C. and Talbot, Nicola L. C.},
title = {Fast exact leave-one-out cross-validation of least-squares support vector machines},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2002)},
year = 2002,
month = APR,
pages = {149--154},
address = {Bruges, Belgium},
abstract = {Model selection methods for kernel machines often seek to minimise
an upper bound on the leave-one-out cross-validation error. This
paper describes an efficient algorithm for \emph{exact} leave-one-out
cross-validation of least-squares support vector machines, in both
classification and regression settings. The proposed method exploits
the considerable redundancy in the family of systems of linear equations
to be solved in explicit computation of the leave-one-out error.
The efficiency of the proposed approach is demonstrated using real-world
and synthetic benchmark datasets}
}
Cawley, GC and Talbot, NLC. A Greedy Training Algorithm for Sparse Least-Squares Support Vector Machines, in Proceedings of the International Conference on Artificial Neural Networks (ICANN-2002). 27-30 Aug 2002. Lecture Notes in Computer Science (LNCS). Springer. Vol. 2415, pp. 681–686.
@inproceedings{Cawley2002c,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {A Greedy Training Algorithm for Sparse Least-Squares Support Vector Machines},
booktitle = {Proceedings of the International Conference on Artificial Neural Networks (ICANN-2002)},
series = {Lecture Notes in Computer Science (LNCS)},
year = 2002,
month = AUG,
volume = {2415},
pages = {681--686},
publisher = {Springer},
address = {Madrid, Spain},
abstract = {Suykens \emph{et al.}\ describes a form of kernel ridge regression
known as the least-squares support vector machine (LS-SVM). In this
paper, we present a simple, but efficient, greedy algorithm for constructing
near optimal sparse approximations of least-squares support vector
machines, in which at each iteration the training pattern minimising
the regularised empirical risk is introduced into the kernel expansion.
The proposed method demonstrates superior performance when compared
with the pruning technique described by Suykens \emph{et al.}, over
the motorcycle and Boston housing datasets}
}
Cawley, GC and Talbot, NLC. Improved Sparse Least-Squares Support Vector Machines, Neurocomputing. Oct 2002. Vol. 48, pp. 1025–1031.
@article{Cawley2002b,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Improved Sparse Least-Squares Support Vector Machines},
journal = {Neurocomputing},
year = 2002,
month = OCT,
volume = {48},
pages = {1025--1031},
doi = {10.1016/S0925-2312(02)00606-9},
abstract = {Suykens \emph{et al.}\ describe a weighted least-squares formulation
of the support vector machine for regression problems and presents
a simple algorithm for sparse approximation of the typically fully
dense kernel expansions obtained using this method. In this paper,
we present an improved method for achieving sparsity in least-squares
support vector machines, which takes into account the residuals for
all training patterns, rather than only those incorporated in the
sparse kernel expansion. The superiority of this algorithm is demonstrated
on the motorcycle and Boston housing datasets}
}
Cawley, GC and Talbot, NLC. Reduced rank kernel ridge regression, Neural Processing Letters. Dec 2002. Vol. 16(3), pp. 293–302.
@article{Cawley2002f,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Reduced rank kernel ridge regression},
journal = {Neural Processing Letters},
year = 2002,
month = DEC,
volume = {16},
number = {3},
pages = {293--302},
doi = {10.1023/A:1021798002258},
abstract = {Ridge regression is a classical statistical technique that attempts
to address the bias-variance trade-off in the design of linear regression
models. A reformulation of ridge regression in dual variables permits
a non-linear form of ridge regression via the well-known ``kernel
trick''. Unfortunately, unlike support vector regression models,
the resulting kernel expansion is typically fully dense. In this
paper, we introduce a reduced rank kernel ridge regression (RRKRR)
algorithm, capable of generating an optimally sparse kernel expansion
that is functionally identical to that resulting from conventional
kernel ridge regression (KRR). The proposed method is demonstrated
to out-perform an alternative sparse kernel ridge regression algorithm
on the Motorcycle and Boston Housing benchmarks}
}
Cawley, GC and Talbot, NLC. Efficient cross-validation of kernel Fisher discriminant classifiers, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003). 23-25 Apr 2003. pp. 241–246.
@inproceedings{Cawley2003a,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Efficient cross-validation of kernel Fisher discriminant classifiers},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003)},
year = 2003,
month = APR,
pages = {241--246},
address = {Bruges, Belgium}
}
Cawley, GC, Talbot, NLC, Foxall, RJ, Dorling, SR and Mandic, DP. Unbiased Estimation of Conditional Variance in Heteroscedastic Kernel Ridge Regression, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003). 23-25 Apr 2003. pp. 209–214.
@inproceedings{Cawley2003b,
author = {Cawley, Gavin C. and Talbot, Nicola L. C. and Foxall, Robert J. and Dorling, Stephen R. and Mandic, Danilo P.},
title = {Unbiased Estimation of Conditional Variance in Heteroscedastic Kernel Ridge Regression},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2003)},
year = 2003,
month = APR,
pages = {209--214},
address = {Bruges, Belgium}
}
Cawley, GC and Talbot, NLC. Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers, Pattern Recognition. Nov 2003. Vol. 36(11), pp. 2585–2592.
@article{Cawley2003d,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Efficient leave-one-out cross-validation of kernel {F}isher discriminant classifiers},
journal = {Pattern Recognition},
year = 2003,
month = NOV,
volume = {36},
number = {11},
pages = {2585--2592},
doi = {10.1016/S0031-3203(03)00136-5}
}
Cawley, GC, Talbot, NLC, Foxall, RJ, Dorling, SR and Mandic, DP. Heteroscedastic kernel ridge regression, Neurocomputing. Mar 2004. Vol. 57, pp. 105–124.
@article{Cawley2004e,
author = {Cawley, Gavin C. and Talbot, Nicola L. C. and Foxall, Robert J. and Dorling, Stephen R. and Mandic, Danilo P.},
title = {Heteroscedastic kernel ridge regression},
journal = {Neurocomputing},
year = 2004,
month = MAR,
volume = {57},
pages = {105--124},
doi = {10.1016/j.neucom.2004.01.005},
abstract = {In this paper we extend a form of kernel ridge regression (KRR) for
data characterised by a heteroscedastic (i.e.\ input dependent variance)
Gaussian noise process, introduced in Foxall et al.\ \cite{Foxall2002a}.
It is shown that the proposed heteroscedastic kernel ridge regression
model can give a more accurate estimate of the conditional mean of
the target distribution than conventional KRR and also provides an
indication of the spread of the target distribution (i.e.\ predictive
error bars). The leave-one-out cross-validation estimate of the conditional
mean is used in fitting the model of the conditional variance in
order to overcome the inherent bias in maximum likelihood estimates
of the variance. The benefits of the proposed model are demonstrated
on synthetic and real-world benchmark data sets and for the task
of predicting episodes of poor air quality in an urban environment.}
}
Cawley, GC and Talbot, NLC. Sparse Bayesian kernel logistic regression, in Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2004). 28-30 Apr 2004. pp. 133–138.
@inproceedings{Cawley2004b,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Sparse {B}ayesian kernel logistic regression},
booktitle = {Proceedings of the European Symposium on Artificial Neural Networks (ESANN-2004)},
year = 2004,
month = APR,
pages = {133--138},
address = {Bruges, Belgium}
}
Cawley, GC and Talbot, NLC. Efficient model selection for kernel logistic regression, in Proceedings of the 17th International Conference on Pattern Recognition (ICPR-2004). 23-26 Aug 2004. Vol. 2, pp. 439–442.
@inproceedings{Cawley2004a,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Efficient model selection for kernel logistic regression},
booktitle = {Proceedings of the 17th International Conference on Pattern Recognition (ICPR-2004)},
year = 2004,
month = AUG,
volume = {2},
pages = {439--442},
address = {Cambridge, United Kingdom},
doi = {10.1109/ICPR.2004.1334249}
}
Kamel Saadi and Nicola L. C. Talbot and Gavin C. Cawley. Optimally regularised kernel Fisher discriminant analysis, in Proceedings of the 17th International Conference on Pattern Recognition (ICPR-2004). 23-26 Aug 2004. Vol. 2, pp. 427–430.
@inproceedings{Saadi2004a,
author = {Kamel Saadi and Nicola L. C. Talbot and Gavin C. Cawley},
title = {Optimally regularised kernel {F}isher discriminant analysis},
booktitle = {Proceedings of the 17th International Conference on Pattern Recognition (ICPR-2004)},
year = 2004,
month = AUG,
volume = {2},
pages = {427--430},
address = {Cambridge, United Kingdom},
doi = {10.1109/ICPR.2004.1334245}
}
Cawley, GC and Talbot, NLC. Fast leave-one-out cross-validation of sparse least-squares support vector machines, Neural Networks. Dec 2004. Vol. 17, pp. 1467–1475.
@article{Cawley2004c,
author = {Cawley, Gavin C. and Talbot, Nicola L. C.},
title = {Fast leave-one-out cross-validation of sparse least-squares support vector machines},
journal = {Neural Networks},
year = 2004,
month = DEC,
volume = {17},
pages = {1467--1475},
doi = {10.1016/j.neunet.2004.07.002}
}
Cawley, G, Talbot, N, Janacek, G and Peck, M. Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis, in Deterministic and Statistical Methods in Machine Learning. 2005. Lecture Notes in Computer Science. Springer Berlin / Heidelberg. Vol. 3635, pp. 37–55.
@incollection{Cawley2005b,
author = {Cawley, Gavin and Talbot, Nicola and Janacek, Gareth and Peck, Michael},
title = {Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis},
booktitle = {Deterministic and Statistical Methods in Machine Learning},
series = {Lecture Notes in Computer Science},
editor = {Winkler, Joab and Niranjan, Mahesan and Lawrence, Neil},
year = 2005,
volume = {3635},
pages = {37--55},
publisher = {Springer Berlin / Heidelberg},
doi = {10.1007/11559887_3}
}
Cawley, GC and Talbot, NLC. The evidence framework applied to sparse kernel logistic regression, Neurocomputing. 2005. Vol. 64, pp. 119–135.
@article{Cawley2005f,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {The evidence framework applied to sparse kernel logistic regression},
journal = {Neurocomputing},
year = 2005,
volume = {64},
pages = {119--135},
doi = {10.1016/j.neucom.2004.11.021}
}
Flom, P, Hagen, H, Hogg, J, Talbot, N, Taylor, P, Thiele, C and Walden, D. What is TeX?, The PracTeX Journal. 2005. Vol. 3.
@article{Flom2005,
author = {Peter Flom and Hans Hagen and Joe Hogg and Nicola Talbot and Philip Taylor and Christina Thiele and David Walden},
title = {What is {\TeX}?},
journal = {The Prac{\TeX}\ Journal},
year = 2005,
volume = {3},
url = {http://tug.org/pracjourn/2005-3/walden-whatis}
}
Cawley, GC and Talbot, NLC. Sparse Bayesian learning and the relevance multi-layer perceptron network, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2005). Jul-Aug 2005.
@inproceedings{Cawley2005c,
author = {Cawley, Gavin C. and Talbot, Nicola L. C.},
title = {Sparse {B}ayesian learning and the relevance multi-layer perceptron network},
booktitle = {Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2005)},
year = 2005,
month = JUL # {--} # AUG,
address = {Montreal, Canada}
}
Cawley, GC and Talbot, NLC. A simple trick for constructing Bayesian formulations of sparse kernel learning methods, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2005). Jul-Aug 2005. pp. 1425–1430.
@inproceedings{Cawley2005d,
author = {Cawley, Gavin C. and Talbot, Nicola L. C.},
title = {A simple trick for constructing {B}ayesian formulations of sparse kernel learning methods},
booktitle = {Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2005)},
year = 2005,
month = JUL # {--} # AUG,
pages = {1425--1430},
address = {Montreal, Canada}
}
Cawley, GC and Talbot, NLC. Constructing Bayesian formulations of sparse kernel learning methods, Neural Networks. Jul-Aug 2005. Vol. 18, issues 5--6, pp. 674–683.
@article{Cawley2005e,
author = {Cawley, Gavin C. and Talbot, Nicola L. C.},
title = {Constructing {B}ayesian formulations of sparse kernel learning methods},
journal = {Neural Networks},
year = 2005,
month = JUL # {--} # AUG,
volume = {18, issues 5--6},
pages = {674--683},
doi = {10.1016/j.neunet.2005.06.002}
}
Cawley, GC, Talbot, NLC, Janacek, GJ and Peck, MW. Sparse Bayesian kernel survival analysis for modelling the growth domain of microbial pathogens, IEEE Transactions on Neural Networks. Mar 2006. Vol. 17(2), pp. 471–481.
@article{Cawley2006b,
author = {Gavin C. Cawley and Nicola L. C. Talbot and Gareth J. Janacek and Mike W. Peck},
title = {Sparse {B}ayesian kernel survival analysis for modelling the growth domain of microbial pathogens},
journal = {IEEE Transactions on Neural Networks},
year = 2006,
month = MAR,
volume = {17},
number = {2},
pages = {471--481},
doi = {10.1109/TNN.2005.863452}
}
Cawley, GC, Talbot, NLC and Girolami, M. Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation, in Advances in Neural Information Processing Systems 19. 2007. MIT Press.
@inproceedings{Cawley2007a,
author = {Gavin C. Cawley and Nicola L. C. Talbot and Mark Girolami},
title = {Sparse Multinomial Logistic Regression via {B}ayesian {L1} Regularisation},
booktitle = {Advances in Neural Information Processing Systems 19},
editor = {B. Sch\"{o}lkopf and J. C. Platt and T. Hofmann},
year = 2007,
publisher = {MIT Press},
address = {Cambridge, MA}
}
Talbot, NLC. Teaching LaTeX for a staff development course, The PracTeX Journal. 2007. Vol. 4.
@article{Talbot2007,
author = {Nicola L. C. Talbot},
title = {Teaching {\LaTeX} for a staff development course},
journal = {The Prac{\TeX}\ Journal},
year = 2007,
volume = {4},
doi = {10.1007/s10994-008-5055-9},
url = {http://tug.org/pracjourn/2007-4/talbot}
}
Cawley, GC and Talbot, NLC. Preventing over-fitting in model selection via Bayesian regularisation of the hyper-parameters, Journal of Machine Learning Research. Apr 2007. Vol. 8, pp. 841–861.
@article{Cawley2007b,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Preventing over-fitting in model selection via {B}ayesian regularisation of the hyper-parameters},
journal = {Journal of Machine Learning Research},
year = 2007,
month = APR,
volume = {8},
number = {},
pages = {841--861},
url = {http://www.jmlr.org/papers/v8/cawley07a.html}
}
Cawley, GC, Janacek, GJ and Talbot, NLC. Generalised kernel machines, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2007). 12-17 Aug 2007. pp. 1732–1737.
@inproceedings{Cawley2007c,
author = {Gavin C. Cawley and Gareth J. Janacek and Nicola L. C. Talbot},
title = {Generalised kernel machines},
booktitle = {Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2007)},
year = 2007,
month = AUG,
pages = {1732--1737},
address = {Orlando, Florida, USA}
}
Cawley, GC and Talbot, NLC. Agnostic learning versus prior knowledge in the design of kernel machines, in Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2007). 12-17 Aug 2007. pp. 1720–1725.
@inproceedings{Cawley2007d,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Agnostic learning versus prior knowledge in the design of kernel machines},
booktitle = {Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN-2007)},
year = 2007,
month = AUG,
pages = {1720--1725},
address = {Orlando, Florida, USA}
}
Saadi, K, Talbot, NLC, and Cawley, GC. Optimally regularised kernel Fisher discriminant classification, Neural Networks. Sep 2007. Vol. 20(7), pp. 832–841.
@article{Saadi2007,
author = {Kamel Saadi and Nicola L. C. Talbot and Gavin C. Cawley},
title = {Optimally regularised kernel {F}isher discriminant classification},
journal = {Neural Networks},
year = 2007,
month = SEP,
volume = {20},
number = {7},
pages = {832--841},
doi = {10.1016/j.neunet.2007.05.005}
}
Cawley, GC and Talbot, NLC. Efficient approximate leave-one-out cross-validation for kernel logistic regression, Machine Learning. Jun 2008. Vol. 71(2--3), pp. 243–264.
@article{Cawley2008,
author = {Gavin C Cawley and Nicola L C Talbot},
title = {Efficient approximate leave-one-out cross-validation for kernel logistic regression},
journal = {Machine Learning},
year = 2008,
month = JUN,
volume = {71},
number = {2--3},
pages = {243--264},
doi = {10.1007/s10994-008-5055-9}
}
Talbot, NLC. Glossaries and lists, in LaTeX.net. 18 Mar 2009. Originally posted on the LaTeX Community's Know How Section as “Glossaries, Nomenclature, Lists of Symbols and Acronyms”.
@inproceedings{Talbot2009a,
author = {Nicola L. C. Talbot},
title = {Glossaries and lists},
booktitle = {{LaTeX}.net},
year = 2009,
month = MAR,
date = {2009-03-18},
url = {https://latex.net/glossaries-nomenclature-lists-symbols-acronyms/},
note = {Originally posted on the {\LaTeX} Community's Know How Section as “Glossaries, Nomenclature, Lists of Symbols and Acronyms”}
}
Talbot, NLC. Multiple Glossaries, in LaTeX.net. 18 Mar 2009. Originally posted on the LaTeX Community's Know How Section as “Glossaries, Nomenclature, Lists of Symbols and Acronyms”.
@inproceedings{Talbot2009a-part2,
author = {Nicola L. C. Talbot},
title = {Multiple Glossaries},
booktitle = {{LaTeX}.net},
year = 2009,
month = MAR,
date = {2009-03-18},
url = {https://latex.net/multiple-glossaries/},
note = {Originally posted on the {\LaTeX} Community's Know How Section as “Glossaries, Nomenclature, Lists of Symbols and Acronyms”}
}
Talbot, NLC. Glossaries with makeindex or xindy, in LaTeX.net. 19 Mar 2009. Originally posted on the LaTeX Community's Know How Section as “Glossaries, Nomenclature, Lists of Symbols and Acronyms”.
@inproceedings{Talbot2009a-part3,
author = {Nicola L. C. Talbot},
title = {Glossaries with makeindex or xindy},
booktitle = {{LaTeX}.net},
year = 2009,
month = MAR,
date = {2009-03-19},
url = {https://latex.net/glossaries-makeindex-xindy/},
note = {Originally posted on the {\LaTeX} Community's Know How Section as “Glossaries, Nomenclature, Lists of Symbols and Acronyms”}
}
Talbot, NLC. Writing a LaTeX Class File to Produce a Form, in LaTeX.net. 20 Mar 2009. Originally posted on the LaTeX Community's Know How Section.
@inproceedings{Talbot2009b,
author = {Nicola L. C. Talbot},
title = {Writing a {\LaTeX} Class File to Produce a Form},
booktitle = {{LaTeX}.net},
year = 2009,
month = MAR,
date = {2009-03-20},
url = {https://latex.net/class-form/},
note = {Originally posted on the {\LaTeX} Community's Know How Section}
}
Talbot, N. Talbot packages: An overview, TUGboat. 2010. Vol. 31(1).
@article{tugboat2010,
author = {Nicola Talbot},
title = {Talbot packages: An overview},
journal = {TUGboat},
year = 2010,
volume = {31},
number = {1},
url = {http://tug.org/TUGboat/tb31-1/tb97talbot.pdf}
}
Cawley, GC and Talbot, NLC. On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation, Journal of Machine Learning Research. Jul 2010. Vol. 11, pp. 2079–2107.
@article{Cawley2010,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation},
journal = {Journal of Machine Learning Research},
year = 2010,
month = JUL,
volume = {11},
pages = {2079--2107},
url = {http://jmlr.csail.mit.edu/papers/v11/cawley10a.html}
}
Talbot, NLC. LaTeX for Complete Novices, Sep 2012. Dickimaw LaTeX Series. Dickimaw Books. Vol. 1.
@book{Talbot2012a,
author = {Nicola L. C. Talbot},
title = {{\LaTeX} for Complete Novices},
series = {Dickimaw {\LaTeX} Series},
year = 2012,
month = SEP,
volume = {1},
publisher = {Dickimaw Books},
url = {https://www.dickimaw-books.com/latex/novices/},
isbn = {978-1-909440-00-5}
}
Talbot, NLC. Creating a glossary without using an external indexing application, in LaTeX.net. Sep 2012. Originally posted on the LaTeX Community's Know How Section.
@inproceedings{Talbot2012c,
author = {Nicola L. C. Talbot},
title = {Creating a glossary without using an external indexing application},
booktitle = {{LaTeX}.net},
year = 2012,
month = SEP,
url = {https://latex.net/glossary-without-external-app/},
note = {Originally posted on the {\LaTeX} Community's Know How Section}
}
Talbot, NLC and Pritchett, M. The Foolish Hedgehog, Nov 2012. Dickimaw Books.
@book{Talbot2012b,
author = {Nicola L C Talbot and Magdalene Pritchett},
title = {The Foolish Hedgehog},
year = 2012,
month = NOV,
publisher = {Dickimaw Books},
url = {https://www.dickimaw-books.com/fiction/kids/hedgehog/},
isbn = {978-1-909440-01-2}
}
Talbot, NLC. Using LaTeX to Write a PhD Thesis, Mar 2013. Dickimaw LaTeX Series. Dickimaw Books. Vol. 2.
@book{Talbot2013a,
author = {Nicola L. C. Talbot},
title = {Using {\LaTeX} to Write a {PhD} Thesis},
series = {Dickimaw {\LaTeX} Series},
year = 2013,
month = MAR,
volume = {2},
publisher = {Dickimaw Books},
url = {https://www.dickimaw-books.com/latex/thesis/},
isbn = {978-1-909440-02-9}
}
Talbot, NLC and Pritchett, M. Quack, Quack, Quack. Give My Hat Back!, May 2013. Dickimaw Books.
@book{Talbot2013b,
author = {Nicola L C Talbot and Magdalene Pritchett},
title = {Quack, Quack, Quack. Give My Hat Back!},
year = 2013,
month = MAY,
publisher = {Dickimaw Books},
url = {https://www.dickimaw-books.com/fiction/kids/duck/},
isbn = {978-1-909440-03-6}
}
Talbot, NLC, Pritchett, M and Medina, G. Cuac, Cuac, Cuac. ¡Devuélveme mi sombrero ya!, Sep 2013. Dickimaw Books.
@book{Talbot2013c,
author = {Nicola L C Talbot and Magdalene Pritchett and Gonzalo Medina},
title = {Cuac, Cuac, Cuac. !`Devu\'elveme mi sombrero ya!},
year = 2013,
month = SEP,
publisher = {Dickimaw Books},
url = {https://www.dickimaw-books.com/fiction/kids/duck-es/},
isbn = {978-1-909440-06-7}
}
Talbot, NLC. I've Heard the Mermaid Sing, Nov 2013. Dickimaw Books. E-book short story.
@book{Talbot2013d,
author = {Nicola L. C. Talbot},
title = {I've Heard the Mermaid Sing},
year = 2013,
month = NOV,
publisher = {Dickimaw Books},
url = {https://www.dickimaw-books.com/fiction/shortstories/mermaid/},
isbn = {978-1-909440-04-3},
note = {E-book short story}
}
Talbot, N. Book review: Let's Learn LaTeX, by S. Parthasarathy, TUGboat. 2014. Vol. 35(3).
@article{tugboat2014,
author = {Nicola Talbot},
title = {Book review: \emph{Let's Learn \LaTeX}, by {S. Parthasarathy}},
journal = {TUGboat},
year = 2014,
volume = {35},
number = {3},
url = {http://tug.org/TUGboat/tb35-3/tb111reviews-partha.pdf}
}
Talbot, NLC. The Private Enemy, 2014. Dickimaw Books.
@book{Talbot2014b,
author = {Nicola L. C. Talbot},
title = {The Private Enemy},
year = 2014,
publisher = {Dickimaw Books},
url = {https://www.dickimaw-books.com/fiction/crime/the-private-enemy/},
isbn = {978-1-909440-05-0}
}
Cawley, GC and Talbot, NLC. Kernel learning at the first level of inference, Neural Networks. May 2014. Vol. 53, pp. 69–80.
@article{Cawley2014a,
author = {Gavin C. Cawley and Nicola L. C. Talbot},
title = {Kernel learning at the first level of inference},
journal = {Neural Networks},
year = 2014,
month = MAY,
volume = {53},
pages = {69--80},
doi = {10.1016/j.neunet.2014.01.011}
}
Talbot, NLC. Using LaTeX for Administrative Purposes, Sep 2014. Dickimaw LaTeX Series. Dickimaw Books. Vol. 3.
@book{Talbot2014a,
author = {Nicola L. C. Talbot},
title = {Using {\LaTeX} for Administrative Purposes},
series = {Dickimaw {\LaTeX} Series},
year = 2014,
month = SEP,
volume = {3},
publisher = {Dickimaw Books},
url = {https://www.dickimaw-books.com/latex/admin/},
isbn = {978-1-909440-07-4}
}
Talbot, N. Localisation of TeX documents: tracklang, TUGboat. 2016. Vol. 37(3).
@article{tugboat2016,
author = {Nicola Talbot},
title = {Localisation of {\TeX} documents: tracklang},
journal = {TUGboat},
year = 2016,
volume = {37},
number = {3},
url = {http://www.tug.org/TUGboat/tb37-3/tb117talbot.pdf}
}
Talbot, N. Testing indexes: testidx.sty, TUGboat. 2017. Vol. 38(3).
@article{tugboat2017,
author = {Nicola Talbot},
title = {Testing indexes: testidx.sty},
journal = {TUGboat},
year = 2017,
volume = {38},
number = {3},
url = {http://tug.org/TUGboat/tb38-3/tb120talbot.pdf}
}
Talbot, N. Indexing, glossaries and bib2gls, TUGboat. 2019. Vol. 40(1).
@article{tugboat2019,
author = {Nicola Talbot},
title = {Indexing, glossaries and bib2gls},
journal = {TUGboat},
year = 2019,
volume = {40},
number = {1},
url = {http://tug.org/TUGboat/tb40-1/tb124talbot-bib2gls.pdf}
}
Talbot, N. bib2gls: selection, cross-references and locations, TUGboat. 2020. Vol. 41(3).
@article{tugboat2020,
author = {Nicola Talbot},
title = {bib2gls: selection, cross-references and locations},
journal = {TUGboat},
year = 2020,
volume = {41},
number = {3},
url = {https://tug.org/TUGboat/tb41-3/tb129talbot-bib2gls-more.pdf}
}
Talbot, NLC. Sorting Glossaries with bib2gls, in LaTeX.net. 19 Jul 2020.
@inproceedings{Talbot2020,
author = {Nicola L. C. Talbot},
title = {Sorting Glossaries with bib2gls},
booktitle = {{LaTeX}.net},
year = 2020,
month = JUL,
date = {2020-07-19},
url = {https://latex.net/sorting-glossaries-with-bib2gls/}
}
Talbot, N. bib2gls: Standalone entries and repeated lists (a little book of poisons), TUGboat. 2022. Vol. 43(1).
@article{tugboat2022,
author = {Nicola Talbot},
title = {bib2gls: Standalone entries and repeated lists (a little book of poisons)},
journal = {TUGboat},
year = 2022,
volume = {43},
number = {1},
url = {https://tug.org/TUGboat/tb43-1/tb133talbot-bib2gls-reorder.pdf}
}
Talbot, NLC. Unsocial Media, 12 May 2023. Dickimaw Books. E-book short story.
@book{Talbot2023a,
author = {Nicola L. C. Talbot},
title = {Unsocial Media},
year = 2023,
month = MAY,
date = {2023-05-12},
publisher = {Dickimaw Books},
url = {https://www.dickimaw-books.com/fiction/shortstories/unsocial-media/},
isbn = {978-1-909440-12-8},
note = {E-book short story}
}