Publications

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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.}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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.}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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”}
}  

@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”}
}  

@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”}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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}
}  

@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/}
}  

@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}
}  

@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}
}