人工智能 > 文章列表
深度学习研究进展 (浏览次数:468)
发表于2015-5-30 12:47:00
深度学习研究进展
Research Progress on Deep Learning
  

DOI:10.11896/j.issn.1002-137X.2015.05.006

基金项目:本文受国家重点基础研究发展规划(973计划)(2013CB329502),国家自然科学基金(61379101)资助
作者 单位 E-mail
郭丽丽  中国矿业大学计算机科学与技术学院 徐州221116   
丁世飞  中国矿业大学计算机科学与技术学院 徐州221116;中国科学院计算技术研究所智能信息处理重点实验室 北京100190  dingsf@cumt.edu.cn 
中文摘要:
      深度学习(Deep Learning) 是一个近几年备受关注的研究领域,在机器学习中起着重要的作用。如果说浅层学习是机器学习的一次浪潮,那么深度学习作为机器学习的一个新领域,将掀起机器学习的又一次浪潮。深度学习通过建立、模拟人脑的分层结构来实现对外部输入的数据进行从低级到高级的特征提取,从而能够解释外部数据。首先介绍了深度学习的由来,分析了浅层学习存在的弊端;其次列举了深度学习的经典方法,主要以监督学习和无监督学习来展开介绍;然后对深度学习的最新研究进展及其应用进行了综述;最后总结了深度学习发展所面临的问题。
英文摘要:
      Deep learning plays an important role in machine learning.If shallow learning is a wave of machine learning, as a new field of machine learning,the deep learning will set off another wave of machine learning.Deep learning establishes and simulates the human brain’s hierarchical structure to extract the external input data’s features from lower to higher,which can explain the external data.Firstly,this paper discussed the origin of deep learning.Secondly,it described the common methods of deep learning illustrated by the example of supervised lear-ning and unsupervised learning.Then it generalized deep learning’s recent research and applications.Finally,it concluded the problems of development.
查看全文  
参考文献(共56条):
[1] 丁世飞.人工智能[M].北京:清华大学出版社,2011
[2] 史忠值.神经网络[M].北京:高等教育出版社,2009
[3] Rumelhart D,Hinton G,Williams R.Learning representationsby back-propagating errors[J].Nature,1986,323(6088):533-536
[4] 余凯,贾磊,陈雨强.深度学习的昨天、今天和明天[J].计算机研究与发展,2013,50(9):1799-1804
[5] Hinton G,Salakhutdinov R.Reducing the dimensionality of data with neural networks[J].Science,2006,313(5786):504-507
[6] Ding Shi-fei,Zhang Yan-an,Chen Jin-rong,et al.Research onUsing Genetic Algorithms to Optimize Elman Neural Networks[J].Neural Computing and Applications,2013,23(2):293-297
[7] Ding Shi-fei,Jia Wei-kuan,Su Chun-yang,et al.Research ofNeural Network Algorithm Based on Factor Analysis and Cluster Analysis[J].Neural Computing and Applications,2011,20(2):297-302
[8] Lee T S,Mumford D.Hierarchical Bayesian inference in the vi-sual cortex[J].Optical Society of America,2003,20(7):1434-1448
[9] Serre T,Wolf L,Bileschi S,et al.Robust object recognition with cortex-like mechanisms[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2007,29(3):411-426
[10] Lee T S,Mumford D,Romero R,et al.The role of the primary visual cortex in higher level vision[J].Vision Research,1998,38 (15):2429-2454
[11] Bengio Y.Learning deep architectures for AI[J].Foundations and Trends in Machine Learning,2009,2(1):1-127
[12] Bengio Y,LeCun Y.Scaling learning algorithms towards AI[M]∥Bottou L,Chapelle O,Decoste D,et al.Large-Scale Kernel Machines.Cambridge:MIT Press,2007:321-358
[13] 李海峰,李纯果.深度学习结构和算法比较分析[J].河北大学学报:自然科学版,2012,32(5):538-544
[14] Hinton G E.Learning distributed representations of concepts[C]∥Proc.of the 8th Annual Conference of the Cognitive ScienceSociety.1986:1-12
[15] 孙志军,薛磊,许阳明.深度学习研究综述[J].计算机应用研究,2012,29(8):2806-2810
[16] Bengio Y,Delalleau O.On the expressive power of deep architectures[C]∥Proceedings of the 22nd International Conference on Algorithmic Learning Theory.Berlin Heidelberg,2011:18-36
[17] Vincent P,Larochelle H,Lajoie I,et al.Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion[J].Journal of Machine Learning Research,2010,11(12):3371-3408
[18] Hubel D H,Wiesel T N.Receptive Fields,Binocular Interaction and al Architecture in the Cat’s Visual Cortex [J].Journal of Physiology,1962,160:106-154
[19] Fukushima K.Neocognition:A Self-Organizing Neural Network Model for a mechanism of Pattern Recognition Unaffected by Shift in Postion[J].Biological Cybermetics,1980,36:193-202
[20] LeCun Y,Bottou L,Bengio Y,et al.Granient-based learning applied to document recognition[J].Proceedings of IEEE,1988,6(11):2278-2324
楼主

您必须登录后才能进行回复或者发起新的主题