Big Data Deep Learning: Challenges and Perspectives

被引:732
|
作者
Chen, Xue-Wen [1 ]
Lin, Xiaotong [2 ]
机构
[1] Wayne State Univ, Dept Comp Sci, Detroit, MI 48404 USA
[2] Oakland Univ, Dept Comp Sci & Engn, Rochester, MI 48309 USA
来源
IEEE ACCESS | 2014年 / 2卷
关键词
Classifier design and evaluation; feature representation; machine learning; neural nets models; parallel processing; TIME ADAPTIVE CLASSIFIERS; NEURAL-NETWORKS; PATTERN-CLASSIFICATION; BACKPROPAGATION; ARCHITECTURE; STACKING; NETS;
D O I
10.1109/ACCESS.2014.2325029
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Deep learning is currently an extremely active research area in machine learning and pattern recognition society. It has gained huge successes in a broad area of applications such as speech recognition, computer vision, and natural language processing. With the sheer size of data available today, big data brings big opportunities and transformative potential for various sectors; on the other hand, it also presents unprecedented challenges to harnessing data and information. As the data keeps getting bigger, deep learning is coming to play a key role in providing big data predictive analytics solutions. In this paper, we provide a brief overview of deep learning, and highlight current research efforts and the challenges to big data, as well as the future trends.
引用
收藏
页码:514 / 525
页数:12
相关论文
共 50 条
  • [21] Machine learning on big data: Opportunities and challenges
    Zhou, Lina
    Pan, Shimei
    Wang, Jianwu
    Vasilakos, Athanasios V.
    NEUROCOMPUTING, 2017, 237 : 350 - 361
  • [22] Big data and language learning: Opportunities and challenges
    Godwin-Jones, Robert
    LANGUAGE LEARNING & TECHNOLOGY, 2021, 25 (01): : 4 - 19
  • [23] Big Data and Deep Learning for Understanding DoD Data
    Zhao, Ying
    MacKinnon, Douglas J.
    Gallup, Shelley P.
    CrossTalk, 2015, 28 (04): : 4 - 11
  • [24] Deep Learning and Data Sampling with Imbalanced Big Data
    Johnson, Justin M.
    Khoshgoftaar, Taghi M.
    2019 IEEE 20TH INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION FOR DATA SCIENCE (IRI 2019), 2019, : 175 - 183
  • [25] Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives
    Zhou, Zhi-Hua
    Chawla, Nitesh V.
    Jin, Yaochu
    Williams, Graham J.
    IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2014, 9 (04) : 62 - 74
  • [26] Data Management Challenges for Deep Learning
    Raj, Aiswarya
    Bosch, Jan
    Olsson, Helena Holmstrom
    Arpteg, Anders
    Brinne, Bjorn
    2019 45TH EUROMICRO CONFERENCE ON SOFTWARE ENGINEERING AND ADVANCED APPLICATIONS (SEAA 2019), 2019, : 140 - 147
  • [27] Big data and deep learning for RNA biology
    Hwang, Hyeonseo
    Jeon, Hyeonseong
    Yeo, Nagyeong
    Baek, Daehyun
    EXPERIMENTAL AND MOLECULAR MEDICINE, 2024, 56 (06): : 1293 - 1321
  • [28] Editorial: Deep Learning for Big Data Analytics
    Yulei Wu
    Fei Hao
    Sambit Bakshi
    Haojun Huang
    Mobile Networks and Applications, 2021, 26 : 2315 - 2317
  • [29] Deep Learning of Astronomical Features with Big Data
    Lieu, Maggie
    Baines, Deborah
    Giordano, Fabrizio
    Merin, Bruno
    Arviset, Christophe
    Altieri, Bruno
    Conversi, Luca
    Carry, Benoit
    ASTRONOMICAL DATA ANALYSIS SOFTWARE AND SYSTEMS XXVIII, 2019, 523 : 49 - 58
  • [30] Significance of deep learning on big data analytics
    Mao, Jilei
    Mao, Zijun
    CIVIL, ARCHITECTURE AND ENVIRONMENTAL ENGINEERING, VOLS 1 AND 2, 2017, : 1597 - 1600