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.
In this paper, we propose a new framework of machine learning theory, parallel learning,which incorporates and inherits many elements from various existing machine learning theories. Special designs are also presented to deal with some important problems in the machine learning research field, e.g., useful data retrieval from big data using software defined artificial systems, combination of predictive learning and ensemble learning, application of Merton s law to prescriptive learning.
作者
李力
林懿伦
曹东璞
郑南宁
王飞跃
LI Li LIN Yi-Lun CAO Dong-Pu ZHENG Nan-Ning WANG Fei-Yue(National Laboratory for Information Science and Technol- ogy （TNList）, Department of Automation, Tsinghua University, Beijing 100084, China the State Key Laboratory of Man- agement and Control for Complex Systems, Institute of Au- tomation, Chinese Academy of Sciences, Beijing 100190, China University of Chinese Academy of Sciences, Beijing 100049, China Driver Cognition and Automated Driving Labora- tory, Cranfield University, Cranfield MK43 0AL, UK Qing- dao Academy of Intelligent Industries, Qingdao 266000, China Institute of Artificial Intelligence and Robotics （IAIR）, Xi＇an Jiaotong University, Xi＇an 710049, China Research Center of Military Computational Experiments and Parallel Systems, National University of Defense Technology, Changsha 4100739 China)
出处
《自动化学报》
EI
CSCD
北大核心
2017年第1期1-8,共8页
Acta Automatica Sinica
基金
国家自然科学基金(91520301)资助~~
关键词
机器学习
人工智能
平行学习
平行智能
平行系统及理论
Machine learning
artificial intelligence
parallel learning
parallel intelligence
parallel system and theory
