[1]马世龙,乌尼日其其格,李小平.大数据与深度学习综述[J].智能系统学报,2016,11(6):728-742.[doi:10.11992/tis.201611021]
MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11(6):728-742.[doi:10.11992/tis.201611021]
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MA Shilong,WUNIRI Qiqige,LI Xiaoping.Deep learning with big data: state of the art and development[J].CAAI Transactions on Intelligent Systems,2016,11(6):728-742.[doi:10.11992/tis.201611021]
大数据与深度学习综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
11
期数:
2016年第6期
页码:
728-742
栏目:
综述
出版日期:
2017-01-20
- Title:
- Deep learning with big data: state of the art and development
- Keywords:
- big data; machine learning; deep network; deep learning; neural network; artificial intelligence; learning algorithm; derivation tree
- 分类号:
- TP311
- 摘要:
- 大数据时代改变了基于数理统计的传统数据科学，促进了数据分析方法的创新，从机器学习和多层神经网络演化而来的深度学习是当前大数据处理与分析的研究前沿。从机器学习到深度学习，经历了早期的符号归纳机器学习、统计机器学习、神经网络和20世纪末开始的数据挖掘等几十年的研究和实践，发现深度学习可以挖掘大数据的潜在价值。本文给出大数据和深度学习的综述，特别是，给出了各种深层结构及其学习算法之间关联的图谱，给出了深度学习在若干领域应用的知名案例。最后，展望了大数据上深度学习的发展与挑战。
- Abstract:
- As the era of the big data arrives, it is accompanied by profound changes to traditional data science based on statistics. Big data also pushes innovations in the methods of data analysis. Deep learning that evolves from machine learning and multilayer neural networks are currently extremely active research areas. From the symbolic machine learning and statistical machine learning to the artificial neural network, followed by data mining in the 90s, this has built a solid foundation for deep learning (DL) that makes it a notable tool for discovering the potential value behind big data. This survey compactly summarized big data and DL, proposed a generative relationship tree of the major deep networks and the algorithms, illustrated a broad area of applications based on DL, and highlighted the challenges to DL with big data, as well as identified future trends.
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备注/Memo
收稿日期:2016-11-15。
基金项目:国家自然科学基金项目（61003016，61300007，61305054）；科技部基本科研业务费重点科技创新类项目（YWF-14-JSJXY-007）；软件开发环境国家重点实验室自主探索基金项目（SKLSDE-2012ZX-28，SKLSDE-2014ZX-06）.
作者简介:马世龙,男,1953年生,教授、博士生导师、中国人工智能学会常务理事、中国人工智能学会人工智能基础专业委员会主任。主要研究方向为海量信息处理的计算模型、自动推理、软件工程。近年来获得2012年度国防科技进步二等奖等奖项,在国内外学术刊物和学术会议发表论文160多篇;乌尼日其其格,女,1979年生,博士研究生,主要研究方向为云计算与大数据、计算机软件形式化方法;李小平,男,1979年生,博士研究生,主要研究方向为云计算与大数据、计算机软件形式化方法。
通讯作者:李小平.E-mail:lee.rex@163.com.
更新日期/Last Update:
1900-01-01