Kernelized design of experiments
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Date
2009-04-30T10:38:27Z
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Abstract
This paper describes an approach for selecting instances in regression
problems in the cases where observations x are readily available, but obtaining
labels y is hard. Given a database of observations, an algorithm
inspired by statistical design of experiments and kernel methods is presented
that selects a set of k instances to be chosen in order to maximize
the prediction performance of a support vector machine. It is shown that
the algorithm significantly outperforms related approaches on a number
of real-world datasets.