| 面向认知的多源数据学习理论和算法研究进展 |
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| 引用本文： | 杨柳,于剑,刘烨,詹德川.面向认知的多源数据学习理论和算法研究进展[J].软件学报,2017,28(11):2971-2991. |
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| 作者姓名： | 杨柳 于剑 刘烨 詹德川 |
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| 作者单位： | 天津大学 计算机科学与技术学院, 天津 300350,交通数据分析与挖掘北京市重点实验室(北京交通大学), 北京 100044,脑与认知科学国家重点实验室(中国科学院心理研究所), 北京 100101;中国科学院大学 心理学系, 北京 100049,计算机软件新技术国家重点实验室(南京大学), 江苏 南京 210023 |
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| 基金项目： | 国家自然科学基金（61632004，61773198，61702358） |
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| 摘 要： | 多源数据学习在大数据时代具有极其重要的意义.目前，多源数据学习算法研究远远超前于多源数据学习理论研究，经典的机器学习理论难以应用于多源数据学习，更难以提供多源数据学习算法在实际应用中的理论保障.从学习的最终目的是知识这一认知切入点出发，对人类学习的认知机理、机器学习的三大经典理论（计算学习理论、统计学习理论和概率图理论）以及多源数据学习算法设计这3个方面的研究进展进行总结，最后给出未来研究方向的思考.
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| 关 键 词： | 统计学习理论 模式分类 特征空间 认知心理 |
| 收稿时间： | 2017/5/14 0:00:00 |
| 修稿时间： | 2017/6/16 0:00:00 |
Research Progress on Cognitive-Oriented Multi-Source Data Learning Theory and Algorithm |
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| YANG Liu,YU Jian,LIU Ye and ZHAN De-Chuan.Research Progress on Cognitive-Oriented Multi-Source Data Learning Theory and Algorithm[J].Journal of Software,2017,28(11):2971-2991. |
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| Authors: | YANG Liu YU Jian LIU Ye and ZHAN De-Chuan |
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| Affiliation: | School of Computer Science and Technology, Tianjin University, Tianjin 300350, China,Beijing Key Laboratory of Traffic Data Analysis and Mining(Beijing Jiaotong University), Beijing 100044, China,State Key Laboratory of Brain and Cognitive Science(Institute of Psychology, The Chinese Academy of Sciences), Beijing 100101, China;Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China and State Key Laboratory for Novel Software Technology(Nanjing University), Nanjing 210023, China |
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| Abstract: | In the age of big data, learning from multi-source data plays an important role in many real applications. To date, plenty of multi-source data learning algorithms have been proposed, however, they pay little attention to the fundamental theoretic laws. Meanwhile, it is hard for the classical machine learning theories to govern all learning systems, and to further provide a theoretical support for multi-source learning algorithms. From the perspective of knowledge acquisition through learning, a survey is given on the research progress of three key problems:the human cognitive mechanism, three classical machine learning theories (such as computational learning theory, statistical learning theory, and probabilistic graphical model), and the design of multi-source learning algorithms. Future theoretical research issues of multi-source data learning also presented and investigated. |
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| Keywords: | statistical learning theory pattern classification featurespace coginitive psychology |
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