Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty

Joachim van der Herten; Ivo Couckuyt; Dirk Deschrijver; Tom Dhaene

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arXiv · 1608.05225

Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty

Joachim van der Herten·Ivo Couckuyt·Dirk Deschrijver·Tom Dhaene

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Abstract

When approximating a black-box function, sampling with active learning focussing on regions with non-linear responses tends to improve accuracy. We present the FLOLA-Voronoi method introduced previously for deterministic responses, and theoretically derive the impact of output uncertainty. The algorithm automatically puts more emphasis on exploration to provide more information to the models.

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Joachim van der Herten, Ivo Couckuyt, Dirk Deschrijver, Tom Dhaene. 2016-08-18. Active Learning for Approximation of Expensive Functions with Normal Distributed Output Uncertainty. https://arxiv.org/abs/1608.05225

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