Learning curves for Gaussian processes regression: A framework for good approximations

Advances in Neural Information Processing Systems

Volumn , Issue , 2001, Pages

  Malzahn, Dörthe ^a   Opper, Manfred ^a  

^a ASTON UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION; REGRESSION ANALYSIS; STATISTICAL MECHANICS;

AVERAGE CASE; GAUSSIAN PROCESS REGRESSION MODEL; GAUSSIAN PROCESSES REGRESSIONS; INPUT SPACE; LEARNING CURVES; SAMPLE SIZES;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84898952221     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (6)

1. D. J. C. Mackay, Gaussian Processes, A Replacement for Neural Networks, NIPS tutorial 1997, May be obtained from http://wol.ra.phy.cam.ac.uk/pub/mackay/ .
2. C. K. I. Williams and C. E. Rasmussen, Gaussian Processes for Regression, in Neural Information Processing Systems 8, D. S. Touretzky, M. C. Mozer and M. E. Hasselmo eds., 514-520, MIT Press (1996).
3. C. K. I. Williams, Computing with Infinite Networks, in Neural Information Processing Systems 9, M. C. Mozer, M. I. Jordan and T. Petsche, eds., 295-301. MIT Press (1997).
4. D. Barber and C. K. I. Williams, Gaussian Processes for Bayesian Classification via Hybrid Monte Carlo, in Neural Information Processing Systems 9, M. C. Mozer, M. I. Jordan and T. Petsche, eds., 340-346. MIT Press (1997).
5. P. Sollich, Learning curves for Gaussian processes, in Neural Information Processing Systems 11, M. S. Kearns, S. A. Solla and D. A. Cohn, eds. 344-350, MIT Press (1999).
6. L. Csató, E. Fokoué, M. Opper, B. Schottky, and O. Winther. Efficient approaches to Gaussian process classification. In Advances in Neural Information Processing Systems, Volume 12, 2000.