Approximation of Gaussian process regression models after training

ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning

Volumn , Issue , 2008, Pages 427-432

  Suttorp, Thorsten ^a   Igel, Christian ^a  

^a RUHR UNIVERSITY BOCHUM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE SPACE; GAUSSIAN PROCESS REGRESSION MODEL; TRAINING DATA;

GAUSSIAN DISTRIBUTION; MATHEMATICAL MODELS; NEURAL NETWORKS; REGRESSION ANALYSIS;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84887004078     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. A. J. Smola and P. L. Bartlett. Sparse greedy Gaussian process regression. In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, Advances in Neural Information Processing Systems, volume 13, pages 619-625. MIT Press, 2001.
2. M. Seeger, C. K. I. Williams, and N. Lawrence. Fast forward selection to speed up sparse Gaussian process regression. In C. M. Bishop and B. J. Frey, editors, Proceedings of the 9th International Workshop on Artificial Intelligence and Statistics. Society for Artificial Intelligence and Statistics, 2003.
3. E. Snelson and Z. Ghahramani. Sparse Gaussian processes using pseudo-inputs. In Y. Weiss, B. Scḧolkopf, and J. Platt, editors, Advances in Neural Information Processing Systems, volume 18. MIT Press, 2006.
4. J. Quiñonero-Candela, C. E. Rasmussen, and C. K. I. Williams. Approximation methods for Gaussian process regression. In L. Bottou, O. Chapelle, D. DeCoste, and J. Weston, editors, Large-Scale Kernel Machines, pages 203-223. MIT Press, 2007.
5. B. Schölkopf, S. Mika, C. J. C. Burges, P. Knirsch, K.-R. Müller, G. Rätsch, and A. J. Smola. Input space versus feature space in kernel-based methods. IEEE Transactions on Neural Networks, 10(5):1000-1017, 1999.
6. T. Suttorp and C. Igel. Resilient simplification of kernel classifiers. In J. Marques de Sá et al., editors, Proceedings of the 17th International Conference on Artificial Neural Networks (ICANN 2007), volume 4668 of LNCS, pages 139-148. Springer-Verlag, 2007.
7. T. Evgeniou, M. Pontil, and T. Poggio. Regularization networks and support vector machines. Advances in Computational Mathematics, 13(1):1-50, 2000.
8. T. Poggio and S. Smale. The mathematics of learning: Dealing with data. Notices of the American Mathematical Society (AMS), 50(5):537-544, 2003.
9. C. M. Bishop. Pattern Recognition and Machine Learning. Springer-Verlag, 2006.
10. C. E. Rasmussen and C. K. I. Williams. Gaussian Processes for Machine Learning. Springer-Verlag, 2006.
11. C. J. C. Burges. Simplified support vector decision rules. In Proceedings of the 13th International Conference on Machine Learning (ICML 1996), pages 71-77, 1996.
12. B. Schölkopf, P. Knirsch, A. J. Smola, and C. J. C. Burges. Fast approximation of support vector kernel expansions, and an interpretation of clustering as approximation in feature space. In P. Levi, R.-J. Ahlers, F. May, and M. Schanz, editors, DAGM-Symposium, pages 124-132. Springer-Verlag, 1998.
13. J. E. Baker. Reducing bias and inefficiency in the selection algorithm. In J. J. Grefenstette, editor, Proceedings of the Second International Conference on Genetic Algorithms, pages 14-21, 1987.
14. M. Riedmiller. Advanced supervised learning in multi-layer perceptrons - From backpropagation to adaptive learning algorithms. Computer Standards and Interfaces, 16(5):265-278, 1994.
15. C. Igel and M. Hüsken. Empirical evaluation of the improved Rprop learning algorithm. Neurocomputing, 50(C):105-123, 2003.
16. D. J. Newman, S. Hettich, C. L. Blake, and C. J. Merz. UCI repository of machine learning databases, 1998. http://www.ics.uci.edu/mlearn/MLRepository.html.
17. E. Andelíc, M. Schaff̈oner, M. Katz, S. E. Kr̈uger, and A. Wendemuth. Kernel least-squares models using updates of the pseudoinverse. Neural Computation, 18:2928-2935, 2006.