Transductive gaussian process regression with automatic model selection

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4212 LNAI, Issue , 2006, Pages 306-317

  Le, Quoc V ^a,b   Smola, Alex J ^a,b   Gärtner, Thomas ^c   Altun, Yasemin ^d  

^a AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^b CSIRO   (Australia)

^c FRAUNHOFER IAIS   (Germany)

^d TOYOTA TECHNOLOGICAL INSTITUTE AT CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; INFORMATION THEORY; LEARNING ALGORITHMS; MATHEMATICAL MODELS; REGRESSION ANALYSIS;

AUTOMATIC MODEL SELECTION; GAUSSIAN PROCESS REGRESSION; MOMENT MATCHING;

LEARNING SYSTEMS;

EID: 33750358670     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11871842_31     Document Type: Conference Paper

References (23)

1. Zhu, X.: Semi-supervised learning literature survey. Technical Report 1530, Computer Sciences, University of Wisconsin-Madison (2005) http://www.cs.wisc.edu/~jerryzhu/pub/ ssl_survey.pdf.
2. Chapelle, O., Zien, A.: Semi-supervised classification by low density separation. In: Tenth International Workshop on Artificial Intelligence and Statistics. (2005)
3. Zhu, X., Lafferty, J., Ghahramani, Z.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proc. Intl. Conf. Machine Learning. (2003)
4. Smola, A.J., Kondor, I.R.: Kernels and regularization on graphs. In Schölkopf, B., Warmuth, M.K., eds.: Proc. Annual Conf. Computational Learning Theory. Lecture Notes in Computer Science, Springer (2003) 144-158
5. Pozdnoukhov, A., Bengio, S.: Semi-supervised kernel methods for regression estimation. In: IEEE International Conference on Acoustic, Speech, and Signal Processing. (2006)
6. Verbeek, J., Vlassis, N.: Gaussian fields for semi-supervised regression and correspondence learning. Pattern Recognition, special issue on similarity based pattern recognition (2006)
7. Tresp, V.: A Bayesian committee machine. Neural Computation 12(11) (2000) 2719-2741
8. Schwaighofer, A., Tresp, V.: Transductive and inductive methods for approximate guassian process regression. In: Neural Information Processing Systems, MIT Press (2003)
9. Chapelle, O., Vapnik, V., Weston, J.: Transductive inference for estimating values of functions. In: Advances in Neural Information Processing Systems. (1999)
10. Schuurmans, D., Southey, F., Wilkinson, D., Guo, Y.: Metric-based approaches for semisupervised regression and classification. In: Semi-Supervised Learning. MIT Press (2006)
11. Brefeld, U., Gärtner, T., Scheffer, T., Wrobel, S.: Efficient co-regularised least squares regression. In: 23rd International Conference on Machine Learning. (2006)
12. Gärtner, T., Le, Q., Burton, S., Smola, A.J., Vishwanathan, S.V.N.: Large-scale multiclass transduction. In Weiss, Y., Schölkopf, B., Platt, J., eds.: Advances in Neural Information Processing Systems 18, Cambride, MA, MIT Press (2006) 411-418
13. Neal, R.M.: Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. Technical Report Technical Report 9702, Dept. of Statistics (1997)
14. Le, Q.V., Smola, A.J., Canu, S.: Heteroscedastic gaussian process regression. In: Proc. Intl. Conf. Machine Learning. (2005)
15. Williams, C.K.I.: Prediction with Gaussian processes: From linear regression to linear prediction and beyond. In Jordan, M.I., ed.: Learning and Inference in Graphical Models. Kluwer Academic (1998) 599-621
16. Neal, R.M.: Assessing relevance determination methods using delve. In: Neural Networks and Machine Learning, Springer (1998) 97-129
17. Jordan, M.I., Gharamani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. In Jordan, M.I., ed.: Learning in Graphical Models. Kluwer Academic (1998) 105-162
18. Altun, Y., Smola, A.: Divergence minimization and convex duality. In: Proc. Annual Conf. Computational Learning Theory. (2006) to appear.
19. Lütkepohl, H.: Handbook of Matrices. John Wiley and Sons, Chichester (1996)
20. Takeuchi, I., Le, Q., Sears, T., Smola, A.: Nonparametric quantile estimation. Journal of Machine Learning Research (2006) To appear and available at http://sml.nicta.com.au/~quocle.
21. Newman, D., Hettich, S., Blake, C., Merz, C.: UCI repository of machine learning databases (1998)
22. R Development Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2005) ISBN 3-900051-07-0.
23. Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7(1) (2006)