Big Data Deep Learning: Challenges and Perspectives

被引：730

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

页码：514 / 525

页数：12

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