Big Data Deep Learning: Challenges and Perspectives

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, bi...

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Veröffentlicht in:IEEE access Jg. 2; S. 514 - 525
Hauptverfasser: Chen, Xue-Wen, Lin, Xiaotong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Big Data
Classifier design and evaluation
Computer vision
Data processing
Deep learning
feature representation
Information analysis
Machine learning
Natural language processing
neural nets models
parallel processing
Pattern recognition
Speech recognition
ISSN:2169-3536, 2169-3536
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2014.2325029