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基于分歧的半监督学习

周志华

周志华. 基于分歧的半监督学习. 自动化学报, 2013, 39(11): 1871-1878. doi: 10.3724/SP.J.1004.2013.01871
引用本文: 周志华. 基于分歧的半监督学习. 自动化学报, 2013, 39(11): 1871-1878. doi: 10.3724/SP.J.1004.2013.01871
ZHOU Zhi-Hua. Disagreement-based Semi-supervised Learning. ACTA AUTOMATICA SINICA, 2013, 39(11): 1871-1878. doi: 10.3724/SP.J.1004.2013.01871
Citation: ZHOU Zhi-Hua. Disagreement-based Semi-supervised Learning. ACTA AUTOMATICA SINICA, 2013, 39(11): 1871-1878. doi: 10.3724/SP.J.1004.2013.01871

基于分歧的半监督学习

doi: 10.3724/SP.J.1004.2013.01871
基金项目: 

国家重点基础研究发展计划(973计划)(2010CB327903),国家自然科学基金(61073097)资助

详细信息
    作者简介:

    周志华 博士, 南京大学计算机科学与技术系教授, 教育部长江学者特聘教授,IEEE Fellow, IAPR Fellow. 主要研究方向为人工智能, 机器学习, 数据挖掘,模式识别, 多媒体信息检索.E-mail: zhouzh@nju.edu.cn

Disagreement-based Semi-supervised Learning

Funds: 

Supported by National Basic Research Program of China (973 Program) (2010CB327903) and National Natural Science Foundation of China (61073097)

  • 摘要: 传统监督学习通常需使用大量有标记的数据样本作为训练例,而在很多现实问题中,人们虽能容易地获得大批数据样本,但为数据 提供标记却需耗费很多人力物力.那么,在仅有少量有标记数据时,可否通过对大量未标记数据进行利用来提升学习性能呢?为此,半监督学习 成为近十多年来机器学习的一大研究热点.基于分歧的半监督学习是该领域的主流范型之一,它通过使用多个学习器来对未标记数据进行利用, 而学习器间的"分歧"对学习成效至关重要.本文将综述简介这方面的一些研究进展.
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出版历程
  • 收稿日期:  2013-07-05
  • 修回日期:  2013-08-28
  • 刊出日期:  2013-11-20

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