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Active Learning Through Sequential Design, With Applications to Detection of Money Laundering

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  • Deng, Xinwei
  • Joseph, V. Roshan
  • Sudjianto, Agus
  • Wu, C. F. Jeff

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  • Deng, Xinwei & Joseph, V. Roshan & Sudjianto, Agus & Wu, C. F. Jeff, 2009. "Active Learning Through Sequential Design, With Applications to Detection of Money Laundering," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 969-981.
    • Handle: RePEc:bes:jnlasa:v:104:i:487:y:2009:p:969-981
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    Cited by:

    1. Hsu, Hsiang-Ling & Chang, Yuan-chin Ivan & Chen, Ray-Bing, 2019. "Greedy active learning algorithm for logistic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 119-134.
    2. Li, Jingjing & Chen, Zimu & Wang, Zhanfeng & Chang, Yuan-chin Ivan, 2020. "Active learning in multiple-class classification problems via individualized binary models," Computational Statistics & Data Analysis, Elsevier, vol. 145(C).
    3. Daniel R. Cavagnaro & Richard Gonzalez & Jay I. Myung & Mark A. Pitt, 2013. "Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach," Management Science, INFORMS, vol. 59(2), pages 358-375, February.
    4. Bo-Shiang Ke & Yuan-chin Ivan Chang, 2021. "A Model-Free Subject Selection Method for Active Learning Classification Procedures," Journal of Classification, Springer;The Classification Society, vol. 38(3), pages 544-555, October.

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