Learning curves for Gaussian processes

Advances in Neural Information Processing Systems

Volumn , Issue , 1999, Pages 344-350

  Sollich, Peter ^a,b  

^a UNIVERSITY OF EDINBURGH   (United Kingdom)

^b KING'S COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EIGENVALUES AND EIGENFUNCTIONS; GAUSSIAN DISTRIBUTION;

APPROXIMATION SCHEME; COVARIANCE FUNCTION; EIGENVALUE DECOMPOSITION; GAUSSIAN PROCESSES; GENERALIZATION ERROR; GENERALIZATION PERFORMANCE; LEARNING CURVES; SIMPLE EXPRESSION;

GAUSSIAN NOISE (ELECTRONIC);

EID: 84898945810     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. See e.g. D J C MacKay, Gaussian Processes, Tutorial at NIPS 10, and recent papers by Goldberg/Williams/Bishop (in NIPS 10), Williams and Barber/Williams (NIPS 9), Williams/Rasmussen (NIPS 8).
2. C A Michelli and G Wahba. Design problems for optimal surface interpolation. In Z Ziegler, editor, Approximation theory and applications, pages 329-348. Academic Press, 1981.
3. M Opper. Regression with Gaussian processes: Average case performance. In I K Kwok-Yee, M Wong, and D-Y Yeung, editors, Theoretical Aspects of Neural Computation: A Multidisciplinary Perspective. Springer, 1997.
4. C K I Williams and F Vivarelli. An upper bound on the learning curve for Gaussian processes. Submitted for publication.
5. C K I Williams. Prediction with Gaussian processes: From linear regression to linear prediction and beyond. In M I Jordan, editor, Learning and Inference in Graphical Models. Kluwer Academic. In press.
6. E Wong. Stochastic Processes in Information and Dynamical Systems. McGraw-Hill, New York, 1971.
7. W H Press, S A Teukolsky, W T Vetterling, and B P Flannery. Numerical Recipes in C (2nd ed.). Cambridge University Press, Cambridge, 1992.
8. P Sollich. Finite-size effects in learning and generalization in linear perceptrons. Journal of Physics A, 27:7771-7784, 1994.