Learning Curves and Bootstrap Estimates for Inference with Gaussian Processes: A Statistical Mechanics Study

Dörthe Malzahn, Manfred Opper

AbstractWe employ the replica method of statistical physics to study the average
case performance of learning systems. The new feature of our theory is
that general distributions of data can be treated, which enables
applications to real data. For a class of Bayesian prediction models
which are based on Gaussian processes, we discuss Bootstrap estimates
for learning curves.
KeywordsBootstrap, Gaussian processes, learning curves, statistical physics, variational methods
TypeJournal paper [With referee]
JournalComplexity
EditorsH. G. Schuster and K. Pawelzik
Year2003    Vol. 8    No. 4    pp. 57-63
PublisherWiley
Electronic version(s)[ps]
BibTeX data [bibtex]
IMM Group(s)Intelligent Signal Processing


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