Recent advances in Bayesian machine learning

被引:0
|
作者
Zhu, Jun [1 ,2 ,3 ]
Hu, Wenbo [1 ,2 ,3 ]
机构
[1] Sate Key Laboratory of Intelligent Technology and Systems, Tsinghua University, Beijing,100084, China
[2] Tsinghua National Laboratory for Information Science and Technology, Beijing,100084, China
[3] Department of Computer Science and Technology, Tsinghua University, Beijing,100084, China
关键词
Big data;
D O I
10.7544/issn1000-1239.2015.20140107
中图分类号
学科分类号
摘要
With the fast growth of big data, statistical machine learning has attracted tremendous attention from both industry and academia, with many successful applications in vision, speech, natural language, and biology. In particular, the last decades have seen the fast development of Bayesian machine learning, which is now representing a very important class of techniques. In this article, we provide an overview of the recent advances in Bayesian machine learning, including the basics of Bayesian machine learning theory and methods, nonparametric Bayesian methods and inference algorithms, and regularized Bayesian inference. Finally, we also highlight the challenges and recent progress on large-scale Bayesian learning for big data, and discuss on some future directions. ©, 2015, Science Press. All right reserved.
引用
收藏
页码:16 / 26
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