Summary
In this paper, a novel active learning algorithm for design of experiments (DOE) is presented. In this algorithm, a boosting method for regression is firstly used to generate ensemble of learners from existing data. And then the average ensemble ambiguity among the element learners in the ensemble is proposed to determine which data point would be labeled by executing experiments. The results of simulations have shown that when the number of experiment is limited, the algorithm is better compared with traditional passive learning algorithms.
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© 2005 Springer-Verlag Berlin Heidelberg
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Du, T., Zhang, S. (2005). Active Learning with Ensembles for DOE. In: Zhang, W., Tong, W., Chen, Z., Glowinski, R. (eds) Current Trends in High Performance Computing and Its Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27912-1_32
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DOI: https://doi.org/10.1007/3-540-27912-1_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25785-1
Online ISBN: 978-3-540-27912-9
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