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- S. Seo, M. Wallat, T. Graepel,
and K. Obermayer. Gaussian Process Regression: Active Data Selection and
Test Point Rejection.
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In Neural Networks - IJCNN 2000, pages 241-246, 2000.
(FTP Gzipped PostScript, 6 pages, 306 kb)
We consider active data selection and test point rejection
strategies for Gaussian process regression based on the variance of the
posterior over target values. Gaussian process regression is viewed as
transductive regression that provides target distributions for given points
rather than selecting an explicit regression function. Since not only the
posterior mean but also the posterior variance are easily calculated we use
this additional information to two ends: Active data selection is performed
by either querying at points of high estimated posterior variance or at
points that minimize the estimated posterior variance averaged over the input
distribution of interest or --- in a transductive manner --- averaged over
the test set. Test point rejection is performed using the estimated posterior
variance as a confidence measure. We find for both a two-dimensional toy
problem and for a real-world benchmark problem that the variance is a
reasonable criterion for both active data selection and test point
rejection.
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