abshtml.bst : BibTeX style file

Introduction

Example

@string{ESANN = "Proceedings of the European symposium on artifical neural networks"}

@string{ESANNadd = "Bruges, Belgium"}

@inproceedings{Cawley2002a,
   author    = "Cawley, G. C. and Talbot, N. L. C.",
   title     = "Efficient formation of a basis in a kernel induced feature space",
   booktitle = ESANN # "~(ESANN 2002)",
   pages     = "1--6",
   address   = ESANNadd,
   month     = apr,
   year      = 2002,
   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.",
   file      = "esann2002a.pdf"
}

@article{Cawley2002b,
   author    = "Cawley, G. C. and Talbot, N. L. C.",
   title     = "Improved sparse least-squares support vector machines",
   journal   = "Neurocomputing",
   volume    = 48,
   pages     = "1025--1031",
   month     = oct,
   year      = 2002,
   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.",
   file      = "neurocomputing.pdf"
}

@inproceedings{Foxall2002,
   author    = "Foxall, R. J. and Cawley, G. C. and Talbot, N. L. C. and Dorling, S. R. and Mandic, D. P.",
   title     = "Heteroscedastic regularised kernel regression for prediction of episodes of poor air quality",
   booktitle = ESANN # "~(ESANN-2002)",
   pages     = "19--24",
   address   = ESANNadd,
   month     = apr,
   year      = 2002,
   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.",
   file      = "esann2002b.pdf"
}

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Dr Nicola Talbot | School of Computing Sciences | University of East Anglia

N.L.C. Talbot. School of Computing Sciences. University of East Anglia. Last Modified: 21 Mar 2004.