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域自适应学习研究进展

刘建伟 孙正康 罗雄麟

刘建伟, 孙正康, 罗雄麟. 域自适应学习研究进展. 自动化学报, 2014, 40(8): 1576-1600. doi: 10.3724/SP.J.1004.2014.01576
引用本文: 刘建伟, 孙正康, 罗雄麟. 域自适应学习研究进展. 自动化学报, 2014, 40(8): 1576-1600. doi: 10.3724/SP.J.1004.2014.01576
LIU Jian-Wei, SUN Zheng-Kang, LUO Xiong-Lin. Review and Research Development on Domain Adaptation Learning. ACTA AUTOMATICA SINICA, 2014, 40(8): 1576-1600. doi: 10.3724/SP.J.1004.2014.01576
Citation: LIU Jian-Wei, SUN Zheng-Kang, LUO Xiong-Lin. Review and Research Development on Domain Adaptation Learning. ACTA AUTOMATICA SINICA, 2014, 40(8): 1576-1600. doi: 10.3724/SP.J.1004.2014.01576

域自适应学习研究进展

doi: 10.3724/SP.J.1004.2014.01576
基金项目: 

国家重点基础研究发展计划(973计划)(2012CB720500),国家自然科学基金(21006127),中国石油大学(北京)基础学科研究基金(JCXK-2011-07)资助

详细信息
    作者简介:

    孙正康 中国石油大学(北京)地球物理与信息工程学院硕士研究生. 主要研究方向为机器学习,域自适应学习研究.E-mail:sunzhengkang@126.com

    通讯作者:

    刘建伟 博士,中国石油大学(北京)副研究员. 主要研究方向为智能信息处理,机器学习,复杂系统分析,预测与控制,算法分析与设计.E-mail:liujw@cup.edu.cn

Review and Research Development on Domain Adaptation Learning

Funds: 

Supported by National Basic Research Program of China (973 Program) (2012CB720500), National Natural Science Foundation of China (21006127), and Basic Subject Research Fund of China University of Petroleum (JCXK-2011-07)

  • 摘要: 传统的机器学习假设测试样本和训练样本来自同一概率分布. 但当前很多学习场景下训练样本和测试样本可能来自不同的概率分布. 域自 适应学习能够有效地解决训练样本和测试样本概率分布不一致的学习问题,作为 机器学习新出现的研究领域在近几年受到了广泛的关注. 鉴于域自适应学习技术 的重要性,综述了域自适应学习的研究进展. 首先概述了域自适应学习的基本问 题,并总结了近几年出现的重要的域自适应学习方法. 接着介绍了近几年提出的 较为经典的域自适应学习理论和当下域自适应学习的热门研究方向,包括样例加 权域自适应学习、特征表示域自适应学习、参数和特征分解域自适应学习和多 源域自适应学习. 然后对域自适应学习进行了相关的理论分析,讨论了高效的度 量判据,并给出了相应的误差界. 接着对当前域自适应学习在算法、模型结构和 实际应用这三个方面的研究新进展进行了综述. 最后分别探讨了域自适应学习在 特征变换和假设、训练优化、模型和数据表示、NLP 研究中存在的问题这四个方面 的有待进一步解决的问题.
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  • 收稿日期:  2013-09-22
  • 修回日期:  2013-12-31
  • 刊出日期:  2014-08-20

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