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Gavin C. Cawley: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. J. Andrew Bangham, J. R. Hidalgo, Richard Harvey, Gavin C. Cawley
    The Segmentation of Images via Scale-Space Trees. [Citation Graph (0, 0)][DBLP]
    BMVC, 1998, pp:- [Conf]
  2. Barry Theobald, Gavin C. Cawley, Silko Kruse, J. Andrew Bangham
    Towards a low bandwidth talking face using appearance models. [Citation Graph (0, 0)][DBLP]
    BMVC, 2001, pp:- [Conf]
  3. Alison Bosson, Gavin C. Cawley, Yi Chan, Richard Harvey
    Non-retrieval: Blocking Pornographic Images. [Citation Graph (0, 0)][DBLP]
    CIVR, 2002, pp:50-60 [Conf]
  4. Gavin C. Cawley, Nicola L. C. Talbot, Gareth J. Janacek, Michael Peck
    Bayesian Kernel Learning Methods for Parametric Accelerated Life Survival Analysis. [Citation Graph (0, 0)][DBLP]
    Deterministic and Statistical Methods in Machine Learning, 2004, pp:37-55 [Conf]
  5. Gavin C. Cawley, Malcolm R. Haylock, Stephen R. Dorling, Clare Goodess, Phil D. Jones
    Statistical downscaling with artificial neural networks. [Citation Graph (0, 0)][DBLP]
    ESANN, 2003, pp:167-172 [Conf]
  6. Gavin C. Cawley, Nicola L. C. Talbot
    Efficient formation of a basis in a kernel induced feature space. [Citation Graph (0, 0)][DBLP]
    ESANN, 2002, pp:1-6 [Conf]
  7. Gavin C. Cawley, Nicola L. C. Talbot
    Efficient cross-validation of kernel fisher discriminant classifiers. [Citation Graph (0, 0)][DBLP]
    ESANN, 2003, pp:241-246 [Conf]
  8. Gavin C. Cawley, Nicola L. C. Talbot
    Sparse Bayesian kernel logistic regression. [Citation Graph (0, 0)][DBLP]
    ESANN, 2004, pp:133-138 [Conf]
  9. Gavin C. Cawley, Nicola L. C. Talbot, Robert J. Foxall, Stephen R. Dorling, Danilo P. Mandic
    Approximately unbiased estimation of conditional variance in heteroscedastic kernel ridge regression. [Citation Graph (0, 0)][DBLP]
    ESANN, 2003, pp:209-214 [Conf]
  10. Robert J. Foxall, Gavin C. Cawley, Nicola L. C. Talbot, Stephen R. Dorling, Danilo P. Mandic
    Heteroscedastic regularised kernel regression for prediction of episodes of poor air quality. [Citation Graph (0, 0)][DBLP]
    ESANN, 2002, pp:19-24 [Conf]
  11. Kee Khoon Lee, Gavin C. Cawley, Michael W. Bevan
    sparse Bayesian promoter based gene classification. [Citation Graph (0, 0)][DBLP]
    ESANN, 2005, pp:527-532 [Conf]
  12. Kamel Saadi, Gavin C. Cawley, Nicola L. C. Talbot
    Fast exact leave-one-out cross-validation of least-squares Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ESANN, 2002, pp:149-154 [Conf]
  13. Gavin C. Cawley, Stephen R. Dorling
    Reproducing a Subjective Classification Scheme for Atmospheric Circulation Patterns over the United Kingdom Using a Neural Network. [Citation Graph (0, 0)][DBLP]
    ICANN, 1996, pp:281-286 [Conf]
  14. Gavin C. Cawley, Nicola L. C. Talbot
    A Greedy Training Algorithm for Sparse Least-Squares Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:681-686 [Conf]
  15. Robert J. Foxall, Gavin C. Cawley, Stephen R. Dorling, Danilo P. Mandic
    Error Functions for Prediction of Episodes of Poor Air Quality. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:1031-1036 [Conf]
  16. Nicola L. C. Talbot, Gavin C. Cawley
    A Fast Index Assignment Method for Robust Vector Quantization of Image Data. [Citation Graph (0, 0)][DBLP]
    ICIP (3), 1997, pp:674-677 [Conf]
  17. Gavin C. Cawley, Nicola L. C. Talbot
    Efficient Model Selection for Kernel Logistic Regression. [Citation Graph (0, 0)][DBLP]
    ICPR (2), 2004, pp:439-442 [Conf]
  18. Kamel Saadi, Nicola L. C. Talbot, Gavin C. Cawley
    Optimally Regularised Kernel Fisher Discriminant Analysis. [Citation Graph (0, 0)][DBLP]
    ICPR (2), 2004, pp:427-430 [Conf]
  19. Gavin C. Cawley, Nicola L. C. Talbot, Olivier Chapelle
    Estimating Predictive Variances with Kernel Ridge Regression. [Citation Graph (0, 0)][DBLP]
    MLCW, 2005, pp:56-77 [Conf]
  20. Gavin C. Cawley, Nicola L. C. Talbot
    Gene selection in cancer classification using sparse logistic regression with Bayesian regularization. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2006, v:22, n:19, pp:2348-2355 [Journal]
  21. Giuseppe Nunnari, Stephen R. Dorling, Uwe Schlink, Gavin C. Cawley, Robert J. Foxall, Tim Chatterton
    Modelling SO2 concentration at a point with statistical approaches. [Citation Graph (0, 0)][DBLP]
    Environmental Modelling and Software, 2004, v:19, n:10, pp:887-905 [Journal]
  22. Uwe Schlink, Olf Herbarth, Matthias Richter, Stephen R. Dorling, Giuseppe Nunnari, Gavin C. Cawley, Emil Pelikán
    Statistical models to assess the health effects and to forecast ground-level ozone. [Citation Graph (0, 0)][DBLP]
    Environmental Modelling and Software, 2006, v:21, n:4, pp:547-558 [Journal]
  23. Gavin C. Cawley, Nicola L. C. Talbot
    Improved sparse least-squares support vector machines. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2002, v:48, n:1-4, pp:1025-1031 [Journal]
  24. Gavin C. Cawley, Nicola L. C. Talbot
    The evidence framework applied to sparse kernel logistic regression. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:64, n:, pp:119-135 [Journal]
  25. Gavin C. Cawley, Nicola L. C. Talbot, Robert J. Foxall, Stephen R. Dorling, Danilo P. Mandic
    Heteroscedastic kernel ridge regression. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2004, v:57, n:, pp:105-124 [Journal]
  26. Jochen J. Steil, Gavin C. Cawley, Fabrice Rossi
    New Issues in Neurocomputing. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2006, v:69, n:7-9, pp:625-626 [Journal]
  27. Jochen J. Steil, Gavin C. Cawley, Thomas Villmann
    Trends in Neurocomputing at ESANN 2004. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2005, v:64, n:, pp:1-4 [Journal]
  28. Barry Theobald, Silko Kruse, J. Andrew Bangham, Gavin C. Cawley
    Towards a low bandwidth talking face using appearance models. [Citation Graph (0, 0)][DBLP]
    Image Vision Comput., 2003, v:21, n:13-14, pp:1117-1124 [Journal]
  29. Gavin C. Cawley
    On a Fast, Compact Approximation of the Exponential Function. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:9, pp:2009-2012 [Journal]
  30. Gavin C. Cawley, Nicola L. C. Talbot
    Fast exact leave-one-out cross-validation of sparse least-squares support vector machines. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2004, v:17, n:10, pp:1467-1475 [Journal]
  31. Gavin C. Cawley, Nicola L. C. Talbot
    Constructing Bayesian formulations of sparse kernel learning methods. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2005, v:18, n:5-6, pp:674-683 [Journal]
  32. Gavin C. Cawley, Nicola L. C. Talbot
    Reduced Rank Kernel Ridge Regression. [Citation Graph (0, 0)][DBLP]
    Neural Processing Letters, 2002, v:16, n:3, pp:293-302 [Journal]
  33. Gavin C. Cawley, Nicola L. C. Talbot
    Efficient leave-one-out cross-validation of kernel fisher discriminant classifiers. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 2003, v:36, n:11, pp:2585-2592 [Journal]
  34. Barry-John Theobald, J. Andrew Bangham, I. A. Matthews, Gavin C. Cawley
    Near-videorealistic synthetic talking faces: implementation and evaluation. [Citation Graph (0, 0)][DBLP]
    Speech Communication, 2004, v:44, n:1-4, pp:127-140 [Journal]
  35. Olli Yli-Harja, Pertti Koivisto, J. Andrew Bangham, Gavin C. Cawley, Richard Harvey, Ilya Shmulevich
    Simplified implementation of the recursive median sieve. [Citation Graph (0, 0)][DBLP]
    Signal Processing, 2001, v:81, n:7, pp:1565-1570 [Journal]
  36. Gavin C. Cawley, Nicola L. C. Talbot, Mark Girolami
    Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:209-216 [Conf]
  37. Kamel Saadi, Nicola L. C. Talbot, Gavin C. Cawley
    Optimally regularised kernel Fisher discriminant classification. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2007, v:20, n:7, pp:832-841 [Journal]
  38. Gavin C. Cawley, Gareth J. Janacek, Malcolm R. Haylock, Stephen R. Dorling
    Predictive uncertainty in environmental modelling. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 2007, v:20, n:4, pp:537-549 [Journal]

  39. Model Selection for Kernel Probit Regression. [Citation Graph (, )][DBLP]


  40. Agnostic Learning versus Prior Knowledge in the Design of Kernel Machines. [Citation Graph (, )][DBLP]


  41. Generalised Kernel Machines. [Citation Graph (, )][DBLP]


  42. Agnostic Learning vs. Prior Knowledge Challenge. [Citation Graph (, )][DBLP]


  43. Predictive Uncertainty in Environmental Modelling. [Citation Graph (, )][DBLP]


  44. Leave-One-Out Cross-Validation Based Model Selection Criteria for Weighted LS-SVMs. [Citation Graph (, )][DBLP]


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