摘要
基于学习的两个主要研究内容,本文提出了学习的二元分层模式,给出了前端学习、后端学习、前后端组合学习和前后端融合学习的概念,构建了前后端融合学习的理论框架与最优化模型;针对前端学习,模拟大脑的分级工作机制,提出了数据与模型混合驱动的逐层数据再表达的方法;最后,以视觉(图像)学习为例,本文给出了一种数据与模型混合驱动的逐层数据再表达的具体方法.
Based on two research contents of machine learning,a two-element layered model of machine learning is proposed.In addition,the concepts of front-end learning,back-end learning,a combination of front-end and back-end learning,and the fusion of front-end and back-end leaning are presented.Specifically,a framework and optimization model for the fusion of front-end and back-end learning is constructed.For front-end learning,which is a simulated hierarchical working mechanism of the brain,we present a layer-by-layer data re-representation method,which is driven by both data and a model.In addition,we propose a specific implementation of the data re-representation method for visual learning.
作者
郭田德
韩丛英
李明强
Tiande GUO;Congying HAN;Mingqiang LI(University of Chinese Academy of Sciences,Beijing 100049,China;Key Laboratory of Big Data Mining and Knowledge Management,Chinese Academy of Sciences,Beijing 100190,China;Information Science Research Institute,China Electronics Technology Group Corporation,Beijing 100086,China)
出处
《中国科学：信息科学》
CSCD
北大核心
2019年第6期739-759,共21页
Scientia Sinica(Informationis)
基金
国家自然科学基金(批准号:11331012,11731013,11571014)资助项目
关键词
机器学习
模式识别
数据表达
数据与模型混合驱动
machine learning
pattern recognition
data representation
hybrid driven by data and model
