Active Learning with Ensembles for DOE

Tao Du () and Shensheng Zhang ()
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Tao Du: Dept. of Computer Science of Shanghai Jiaotong University
Shensheng Zhang: Dept. of Computer Science of Shanghai Jiaotong University

A chapter in Current Trends in High Performance Computing and Its Applications, 2005, pp 283-287 from Springer

Abstract: 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.

Keywords: Machine learning; active learning; design of experiment; query by boosting (search for similar items in EconPapers)
Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-27912-9_32

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DOI: 10.1007/3-540-27912-1_32

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