Learning curves for Gaussian processes models: fluctuations and universality

Malzahn, Dorthe and Opper, Manfred (2001). Learning curves for Gaussian processes models: fluctuations and universality. IN: Artificial Neural Networks — ICANN 2001. Dorffner, G.; Bischof, H. and Hornik, K. (eds) Lecture Notes in Computer Science, 2130 . Berlin Heidelberg: Springer.

Abstract

Based on a statistical mechanics approach, we develop a method for approximately computing average case learning curves and their sample fluctuations for Gaussian process regression models. We give examples for the Wiener process and show that universal relations (that are independent of the input distribution) between error measures can be derived.

Publication DOI: https://doi.org/10.1007/3-540-44668-0_39
Divisions: ?? 13770100JJ ??
Additional Information: The original publication is available at www.springerlink.com
Event Title: Artificial Neural Networks 2001
Event Type: Other
Event Dates: 2001-08-21 - 2001-08-25
Uncontrolled Keywords: statistical,mechanics,computing average,learning curves,sample fluctuations,Gaussian process regression,Wiener process,universal relations,error measures
Published Date: 2001-01-01

Download

[img]

Export / Share Citation


Statistics

Additional statistics for this record