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SCIENCE CHINA Information Sciences, Volume 59, Issue 1: 012201(2016) https://doi.org/10.1007/s11432-015-5429-0

Unsupervised learning of Dirichlet process mixture models with missing data

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  • ReceivedJun 3, 2015
  • AcceptedJul 2, 2015
  • PublishedDec 2, 2015

Abstract

There is no abstract available for this article.


Funded by

National Natural Science Foundation of China(61273233)

Project of China Ocean Association(DY125-25-02)

Major Scientific Instrument Development Project of National Natural Science Foundation of China(41427806)

Research Foundation for the Doctoral Program of Higher Education(20130002130010)


Acknowledgment

Acknowledgments

This work was supported by Project of China Ocean Association (Grant No. DY125-25-02), Major Scientific Instrument Development Project of National Natural Science Foundation of China (Grant No. 41427806), National Natural Science Foundation of China (Grant No. 61273233), and Research Foundation for the Doctoral Program of Higher Education (Grant No. 20130002130010).


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