ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2015, Vol. 52 ›› Issue (1): 16-26.doi: 10.7544/issn1000-1239.2015.20140107

所属专题: 2015优青专题

• 综述 • 上一篇    下一篇

贝叶斯机器学习前沿进展综述

朱军,胡文波   

  1. (智能技术与系统国家重点实验室(清华大学) 北京 100084) (清华信息科学与技术国家实验室(筹) 北京 100084) (清华大学计算机科学技术系 北京 100084) (dcszj@mail.tsinghua.edu.cn)
  • 出版日期: 2015-01-01
  • 基金资助: 
    基金项目:国家“九七三”重点基础研究发展计划基金项目(2013CB329403,2012CB316301)|国家自然科学基金项目(61322308,61332007)

Recent Advances in Bayesian Machine Learning

Zhu Jun,Hu Wenbo   

  1. (Sate Key Laboratory of Intelligent Technology and Systems (Tsinghua University), Beijing 100084) (Tsinghua National Laboratory for Information Science and Technology, Beijing 100084) (Department of Computer Science and Technology, Tsinghua University, Beijing 100084)
  • Online: 2015-01-01

摘要: 随着大数据的快速发展,以概率统计为基础的机器学习在近年来受到工业界和学术界的极大关注,并在视觉、语音、自然语言、生物等领域获得很多重要的成功应用,其中贝叶斯方法在过去20多年也得到了快速发展,成为非常重要的一类机器学习方法.总结了贝叶斯方法在机器学习中的最新进展,具体内容包括贝叶斯机器学习的基础理论与方法、非参数贝叶斯方法及常用的推理方法、正则化贝叶斯方法等. 最后,还针对大规模贝叶斯学习问题进行了简要的介绍和展望,对其发展趋势作了总结和展望.

关键词: 贝叶斯机器学习, 非参数方法, 正则化方法, 大数据学习, 大数据贝叶斯学习

Abstract: 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.

Key words: Bayesian machine learning, nonparametric methods, regularized methods, learning with big data, big Bayesian learning

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