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Gaussian Process Regression: Active Data Selection and Test Point Rejection
Citation key Seo2000
Author Seo, S. and Wallat, M. and Graepel, T. and Obermayer, K.
Title of Book Neural Networks - IJCNN 2000
Pages 241 – 246
Year 2000
ISBN 0-7695-0619-4
ISSN 1098-7576
DOI 10.1109/IJCNN.2000.861310
Volume 3
Publisher IEEE
Abstract 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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