Efficient approaches to Gaussian Process classification

Advances in Neural Information Processing Systems

Volumn , Issue , 2000, Pages 251-257

  Csató, Lehel ^a   Fokoué, Ernest ^a   Opper, Manfred ^a   Schottky, Bernhard ^a   Winther, Ole ^b  

^a ASTON UNIVERSITY   (United Kingdom)

^b LUND UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

GAUSSIAN DISTRIBUTION; STATISTICAL MECHANICS;

BAYESIAN; BAYESIAN EVIDENCE; GAUSSIAN PROCESS CLASSIFICATIONS; HYPER-PARAMETER; MEAN FIELD; POSTERIOR MEANS; STATISTICAL PHYSICS; TRAINING ERRORS;

GAUSSIAN NOISE (ELECTRONIC);

EID: 0001609938     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Williams C.K.I, and Rasmussen C.E., Gaussian Processes for Regression, in Neural Information Processing Systems 8, Touretzky D.S, Mozer M.C. and Hasselmo M.E. (eds.), 514-520, MIT Press (1996).
2. Neal R.M, Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification, Technical Report 9702, Department of Statistics, University of Toronto (1997).
3. Gibbs M.N. and Mackay D.J.C., Variational Gaussian Process Classifiers, Preprint Cambridge University (1997).
4. Williams C.K.I, and Barber D, Bayesian Classification with Gaussian Processes, IEEE Trans Pattern Analysis and Machine Intelligence, 20 1342-1351 (1998).
5. Opper M. and Winther O. Gaussian Processes for Classification: Mean Field Algorithms, Submitted to Neural Computation, http://www.thep.lu.se/tf2/staff/ winther/ (1999).
6. Zinn-Justin J, Quantum Field Theory and Critical Phenomena, Clarendon Press Oxford (1990).
7. Ripley B.D, Pattern Recognition and Neural Networks, Cambridge University Press (1996).
8. Opper M., Online versus Offline Learning from Random Examples: General Results, Phys. Rev. Lett. 77, 4671 (1996).
9. Online Learning in Neural Networks, Cambridge University Press, D. Saad (ed.) (1998).
10. Gorman R.P and Sejnowski T.J, Analysis of hidden units in a layered network trained to classify sonar targets, Neural Networks 1, (1988).
11. Jaakkola T. and Haussler D. Probabilistic kernel regression, In Online Proceedings of 7-th Int. Workshop on AI and Statistics (1999), http://uncertainty99.microsoft.com/proceedings.htm.