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Active Learning with Ensembles for DOE

In: Current Trends in High Performance Computing and Its Applications

Author

Listed:
  • Tao Du

    (Dept. of Computer Science of Shanghai Jiaotong University)

  • Shensheng Zhang

    (Dept. of Computer Science of Shanghai Jiaotong University)

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.

Suggested Citation

  • Tao Du & Shensheng Zhang, 2005. "Active Learning with Ensembles for DOE," Springer Books, in: Wu Zhang & Weiqin Tong & Zhangxin Chen & Roland Glowinski (ed.), Current Trends in High Performance Computing and Its Applications, pages 283-287, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-27912-9_32
    DOI: 10.1007/3-540-27912-1_32
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