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平行学习——机器学习的一个新型理论框架

李力 林懿伦 曹东璞 郑南宁 王飞跃

李力, 林懿伦, 曹东璞, 郑南宁, 王飞跃. 平行学习——机器学习的一个新型理论框架. 自动化学报, 2017, 43(1): 1-8. doi: 10.16383/j.aas.2017.y000001
引用本文: 李力, 林懿伦, 曹东璞, 郑南宁, 王飞跃. 平行学习——机器学习的一个新型理论框架. 自动化学报, 2017, 43(1): 1-8. doi: 10.16383/j.aas.2017.y000001
LI Li, LIN Yi-Lun, CAO Dong-Pu, ZHENG Nan-Ning, WANG Fei-Yue. Parallel Learning——A New Framework for Machine Learning. ACTA AUTOMATICA SINICA, 2017, 43(1): 1-8. doi: 10.16383/j.aas.2017.y000001
Citation: LI Li, LIN Yi-Lun, CAO Dong-Pu, ZHENG Nan-Ning, WANG Fei-Yue. Parallel Learning——A New Framework for Machine Learning. ACTA AUTOMATICA SINICA, 2017, 43(1): 1-8. doi: 10.16383/j.aas.2017.y000001

平行学习——机器学习的一个新型理论框架

doi: 10.16383/j.aas.2017.y000001
基金项目: 

国家自然科学基金 91520301

详细信息
    作者简介:

    李力 清华大学自动化系副教授.主要研究方向为人工智能和机器学习,智能交通系统和智能汽车.E-mail:li-li@tsinghua.edu.cn

    林懿伦 中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生.主要研究方向为社会计算,智能交通系统,深度学习和强化学习.E-mail:linyilun2014@ia.ac.cn

    曹东璞 英国克兰菲尔德大学驾驶员认知与自动驾驶实验室主任.中科院自动化所客座研究员.主要研究方向为自动驾驶,人车协同,与平行驾驶.E-mail:d.cao@cranfield.ac.uk

    郑南宁 西安交通大学人工智能与机器人研究所教授.中国工程院院士.主要研究方向为模式识别与智能系统,机器视觉与图象处理.E-mail:nnzheng@mail.xjtu.edu.cn

    通讯作者:

    王飞跃 中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员.国防科学技术大学军事计算实验与平行系统技术研究中心主任.主要研究方向为智能系统和复杂系统的建模、分析与控制.本文通信作者.E-mail:feiyue.wang@ia.ac.cn.

Parallel Learning——A New Framework for Machine Learning

Funds: 

Supported by National Natural Science Foundation of China 91520301

More Information
    Author Bio:

    LI Li Associate professor at Depart- ment of Automation, Tsinghua Univer-sity. His research interest covers arti- cial intelligence and machine learning, intelligent transportation systems and intelligent vehicles.

    LIN Yi-Lun Ph.D at the State Key Laboratory of Management and Con-trol for Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers social computing, intelligent transportation systems, deep learning and reinforcement learning.

    CAO Dong-Pu Director of Driver Cognition and Automated Driving Lab- oratory, Cran¯eld University. Visiting professor at Institute of Automation, Chinese Academy of Sciences. His research interest covers automated driving, driver-automation collaboration and parallel driving.

    ZHENG Nan-Ning Professor at Institute of Arti¯cial Intelligence and Robotics (IAIR), Xi0an Jiaotong Uni- versity. Member of Chinese Academy of Engineering. His research interest covers pattern recognition and intelligent systems, computer vision and image processing.

    Corresponding author: WANG Fei-Yue Professor at the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. Director of the Research Center for Computational Experiments and Parallel Systems Technol- ogy, National University of Defense Technology. His re- search interest covers modeling, analysis, and control of intelligent systems and complex systems. Corresponding author of this paper.E-mail:feiyue.wang@ia.ac.cn.
  • 摘要: 本文提出了一种新的机器学习理论框架.该框架结合了现有多种机器学习理论框架的优点,并针对如何使用软件定义的人工系统从大数据提取有效数据,如何结合预测学习和集成学习,以及如何利用默顿定律进行指示学习等目前机器学习领域面临的重要问题进行了特别设计.
  • 图  1  平行学习的理论框架图(虚线上方为通过软件定义的人工系统进行大数据预处理,虚线下方表示基于计算实验的预测学习和集成学习,以及平行控制和指示学习. 细线箭头代表数据生成或数据学习,粗线箭头代表行动和数据之间的交互.)

    Fig.  1  The theoretical framework of parallel learning (The part above the dash line focuses on big data preprocessing using software defined artificial systems; the part beneath the dash line focuses on predictive learning and ensemble learning based computational experiments,as well as parallel control and prescriptive learning. The thin arrows represent either data generation or data learning; the thick arrows present interactions between data and actions.)

    图  2  AlphaGo将现实世界的数据映射到平行世界,进行多线迭代来求取预期行动

    Fig.  2  AlphaGo maps data in realistic world into parallel world and uses multithread iterations to determine the expected actions

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