Infoscience

Conference paper

Fast Gaussian Process Regression Using KD-Trees

The computation required for Gaussian process regression with n training examples is about O(n^3) during training and O(n) for each prediction. This makes Gaussian process regression too slow for large datasets. In this paper, we present a fast approximation method, based on kd-trees, that significantly reduces both the prediction and the training times of Gaussian process regression.

    Keywords: Gaussian process ; KD tree

    Reference

    • EPFL-CONF-161316

    Record created on 2010-12-01, modified on 2016-08-09

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