[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]
点击复制

大数据与深度学习综述

参考文献/References:
[1] TOLLE K M, TANSLEY D, HEY A J G. The fourth paradigm:data-intensive scientific discovery[J]. Proceedings of the IEEE, 2012, 99(8):1334-7.
[2] MASHEY J R. Big Data and the next wave of infra stress[D] Berkeley:University of California, 1997.
[3] MAYER-SCHÖNBERGER V, CUKIER K. A big data:a revolution that will transform how we live, work, and think[M]. Boston:Eamon Dolan, 2013.
[4] HILBERT M, LÓPEZ P. The world’s technological capacity to store, communicate, and compute information[J]. Science, 2011, 332(6025):60-65.
[5] LANEY D. 3D data management:controlling data volume, velocity, and variety[R]. META Group Research Note, 2001.
[6] IDC. IIIS:the "four vs" of big data[EB/OL].[2016-11-11]. http://www.computerworld.com.au/article/396198/iiis_four_vs_big_data/.
[7] SCHROECK M J, SHOCKLEY R, SMART J, et al. Analytics:the real-world use of big data[R]. Oxford:IBM, 2012.
[8] IBM. The four v’s of big data[EB/OL]. 2014[2016-11-11]. http://www.ibmbigdatahub.com/infographic/four-vs-big-data.
[9] 郭平, 王可, 罗阿理, 等. 大数据分析中的计算智能研究现状与展望[J]. 软件学报, 2015, 26(11):3010-3025. GUO Ping, WANG Ke, LUO Ali, et al. Computational intelligence for big data analysis:current status and future prospect[J]. Journal of software, 2015, 26(11):3010-3025.
[10] Gartner. Big data[EB/OL].[2016-11-11]. http://www.gartner.com/it-glossary/big-data/.
[11] MANYIKA J, CHUI M, BROWN B, et al. Big data:the next frontier for innovation, competition, and productivity[R]. Analytics:McKinsey & Company, 2011.
[12] Wikiprdia. Big data[EB/OL]. 2009.[2016-11-11]. https://en.wikipedia.org/wiki/Big_data.
[13] JAMES J. How much data is created every minute?[EB/OL].[2016-11-11]. https://www.domo.com/blog/how-much-data-is-created-every-minute/.
[14] 维克托·迈尔·舍恩伯格, 周涛. 大数据时代生活、工作与思维的大变革[M]. 周涛, 译. 杭州:浙江人民出版社, 2013:136-136.
[15] 孟小峰,慈祥. 大数据管理:概念、技术与挑战[J]. 计算机研究与发展, 2013, 50(1):146-169. MENG Xiaofeng, CI Xiang. Big data management:concepts, techniques and challenges[J]. Journal of computer research and development, 2013, 50(1):146-169.
[16] 王意洁, 孙伟东, 周松, 等. 云计算环境下的分布存储关键技术[J]. 软件学报, 2012, 23(4):962-986. WANG Yijie, SUN Weidong, ZHOU Song, et al. Key technologies of distributed storage for cloud computing[J]. Journal of software, 2012, 23(4):962-986.
[17] GANTZ J, REINSEL D. Extracting value from chaos[R]. Idcemc2 Report, 2011.
[18] 程学旗, 靳小龙, 王元卓, 等. 大数据系统和分析技术综述[J]. 软件学报, 2014, 25(9):1889-1908. CHENG Xueqi, JIN Xiaolong, WANG Yuanzhuo, et al. Survey on big data system and analytic technology[J]. Journal of software, 2014, 25(9):1889-1908.
[19] STUART D. The data revolution:big data, open data, data infrastructures and their consequences[J]. Online information review, 2015, 39(2):272.
[20] LABRINIDIS A, JAGADISH H V. Challenges and opportunities with big data[J]. Proceedings of the vldb endowment, 2012, 5(12):2032-2033.
[21] ABU-MOSTAFA Y S, MAGDON-ISMAIL M, LIN H T. Learning from data:a short course[M]. Chicago:Amlbook, 2012.
[22] 洪家荣. 机器学习--回顾与展望[J]. 计算机科学, 1991, 18(2):1-8. HONG Jiarong. Machine learning-review and vision[J]. Computer science, 1991,18(2):1-8.
[23] SAMUEL A L. Some studies in machine learning using the game of checkers. II-recent progress[J]. Annual review in automatic programming, 1969, 6:1-36.
[24] ROSENBLATT F. The perceptron-a perceiving and recognizing automaton[R]. Ithaca, NY:Cornell Aeronautical Laboratory, 1957.
[25] WIDROW B, LEHR M A. 30 years of adaptive neural networks:perceptron, Madaline, and backpropagation[J]. Proceedings of the IEEE, 1990, 78(9):1415-1442.
[26] MINSKY M, PAPERT S A. Perceptrons:an introduction to computational geometry, expanded edition[M]. Cambridge, Mass:MIT Press, 1988:449-452.
[27] 王珏, 石纯一. 机器学习研究[J]. 广西师范大学学报:自然科学版, 2003, 21(2):1-15. WANG Jue, SHI Chunyi. Investigations on machine learning[J]. Journal of Guangxi normal university:natural science edition, 2003, 21(2):1-15.
[28] CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20(3):273-297.
[29] REYNOLDS D A, ROSE R C, SMITH M J T. A mixture modeling approach to text-independent speaker identification[J]. Journal of the acoustical society of america, 1990, 87(S1):109.
[30] RUMELHART D E, MCCLELLAND J L. Parallel distributed processing:explorations in the microstructure of cognition:foundations[M]. Cambridge, Mass:MIT Press, 1987.
[31] WERBOS P J. Backpropagation through time:what it does and how to do it[J]. Proceedings of the IEEE, 1990, 78(10):1550-1560.
[32] WU Xindong, KUMAR V, QUINLAN J R, et al. Top 10 algorithms in data mining[J]. Knowledge and information systems, 2008, 14(1):1-37.
[33] GORI M, TESI A. on the problem of local minima in backpropagation[J]. IEEE transactions on pattern analysis and machine intelligence, 1992, 14(1):76-86.
[34] FLETCHER L, KATKOVNIK V, STEFFENS F E, et al. Optimizing the number of hidden nodes of a feedforward artificial neural network[C]//Proceedings of 1998 IEEE International Joint Conference Neural Networks. Anchorage, AK:IEEE, 1998, 2:1608-1612.
[35] LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553):436-444.
[36] BENGIO Y, COURVILLE A, VINCENT P. A courville and P vincent, representation learning:a review and new perspectives[J]. IEEE transactions on pattern analysis and machine intelligence, 2013, 35(8):1798-1828.
[37] ACKLEY D H, HINTON G E, SEJNOWSKI T J. A learning algorithm for boltzmann machines[J]. Cognitive science, 1985, 9(1):147-169.
[38] 刘建伟, 刘媛, 罗雄麟. 玻尔兹曼机研究进展[J]. 计算机研究与发展, 2014, 51(1):1-16. LIU Jianwei, LIU Yuan, LUO Xionglin. Research and development on boltzmann machine[J]. Journal of computer research and development, 2014, 51(1):1-16.
[39] SALAKHUTDINOV R, HINTON G. Deep boltzmann machines[J]. Journal of machine learning research, 2009, 5(2):1997-2006.
[40] SMOLENSKY P. Information processing in dynamical systems:foundations of harmony theory[M]. Cambridge, Mass:MIT Press, 1986:194-281.
[41] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[42] HINTON G E. Training products of experts by minimizing contrastive divergence[J]. Neural computation, 2002, 14(8):1771-800.
[43] 张建明, 詹智财, 成科扬, 等. 深度学习的研究与发展[J]. 江苏大学学报:自然科学版, 2015, 36(2):191-200. ZHANG Jianming, ZHAN Zhicai, CHENG Keyang, et al. Review on development of deep learning[J]. Journal of Jiangsu university:natural science edition, 2015, 36(2):191-200.
[44] 孙志远, 鲁成祥, 史忠植, 等. 深度学习研究与进展[J]. 计算机科学, 2016, 43(2):1-8. SUN Zhiyuan, LU Chengxiang, SHI Zhongzhi, et al. Research and advances on deep learning[J]. Computer science, 2016, 43(2):1-8.
[45] SCHMIDHUBER J. Deep learning in neural networks:an overview[J]. Neural networks, 2015,
[46] CHEN H, MURRAY A. A continuous restricted boltzmann machine with a hardware-amenable learning algorithm[J]. Lecture notes in computer science, 2002, 2415:358-363.
[47] LUO Heng, SHEN Ruimin, NIU Changyong. Sparse group restricted boltzmann machines[C]//Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence. San Francisco, California, Usa:AAAI Press, 2010.
[48] LEE H, LARGMAN Y, PHAM P, et al. Unsupervised feature learning for audio classification using convolutional deep belief networks[C]//Advances in Neural Information Processing Systems 22:Conference on Neural Information Processing Systems 2009. Vancouver, British Columbia, Canada, 2009.
[49] HALKIAS X, PARIS S, GLOTIN H. Sparse penalty in deep belief networks:using the mixed norm constraint[J]. Computer science, 2013.
[50] POUGETABADIE J,MIRZA M,XU Bing,et al. Generative adversarial nets[J]. Advances in neural information processing systems,2014,3:2672-2680.
[51] YOSHUA Bengio, PASCAL Lamblin, DAN Popovici, et al. Greedy layer-wise training of deep networks[C]//Proceedings of the Nips, Canada, 2006:153-160.
[52] RANZATO M A, BOUREAU Y L, LECUN Y. Sparse feature learning for deep belief networks[J]. Advances in neural information processing systems, 2007, 20:1185-1192.
[53] VINCENT P, LAROCHELLE H, BENGIO Y, et al. Extracting and composing robust features with denoising autoencoders[C]//Proceedings of the International Conference, F, 2008.
[54] 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):3371-408.
[55] JIANG Xiaojuan, ZHANG Yinghua, ZHANG Wensheng, et al. A novel sparse auto-encoder for deep unsupervised learning[C]//Proceedings of Sixth International Conference on Advanced Computational Intelligence. Hangzhou:IEEE, 2013:256-261.
[56] LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-324.
[57] WANG Wei, OOI B C, YANG Xiaoyan, et al. Effective multi-modal retrieval based on stacked auto-encoders[J]. Proceedings of the VLDB endowment, 2014, 7(8):649-660.
[58] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Advances in neural information processing systems, 2012, 25(2):2012.
[59] ELMAN J L. Finding structure in time[J]. Cognitive science, 1990, 14(2):179-211.
[60] HIHI S E, HC-J M Q, BENGIO Y. Hierarchical recurrent neural networks for long-term dependencies[J]. Advances in neural information processing systems, 1995, 8(493-9.
[61] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural computation, 1997, 9(8):1735-1780.
[62] CHO K, MERRIENBOER B V, BAHDANAU D, et al. On the properties of neural machine translation:encoder-decoder approaches[J]. Computer science, 2014,
[63] MNIH V, HEESS N, GRAVES A, et al. Recurrent models of visual attention[J]. Computer science, 2014, 3(2204-12.
[64] GOODFELLOW I, POUGETABADIE J, MIRZA M, et al. Generative adversarial Nets[J]. Advances in neural information processing systems, 2014, 2672-80.
[65] RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks[J]. Computer science, 2015,
[66] XUE J H, TITTERINGTON D M. Comment on "on discriminative vs. generative classifiers:a comparison of logistic regression and naive Bayes"[J]. Neural processing letters, 2008, 2(3):169-87.
[67] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7):1527-1554.
[68] LECUN Y, JACKEL L D, BOTTOU L, et al. Learning algorithms for classification:a comparison on handwritten digit recognition[M]//OH J H, CHO S. Neural Networks:The Statistical Mechanics Perspective. Singapore:World Scientific, 1995.
[69] LE Q V. Building high-level features using large scale unsupervised learning[C]//Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Vancouver, BC:IEEE, 2013:8595-8598.
[70] NYTIMES. In a big network of computers evidence of machine learning[EB/OL].[2016-11-11].http://www.nytimes.com/2012/06/26/technology/in-a-big-network-of-computers-evidence-of-machine-learning.html?pagewanted=all.
[71] SUN Yi, WANG Xiaogang, TANG Xiaoou. Deep learning face representation from predicting 10,000 classes[C]//Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH:IEEE, 2014.
[72] BBC. Artificial intelligence:Google’s AlphaGo beats Go master lee Se-dol[EB/OL]. 2016.[2016-11-11]. http://www.bbc.com/news/technology-35785875.
[73] SILVER D, HUANG J, MADDISON C J, et al. Mastering the game of go with deep neural networks and tree search[J]. Nature, 2016, 529(7587):484-489.
[74] MOHAMED A R, DAHL G E, HINTON G. Acoustic modeling using deep belief networks[J]. IEEE transactions on audio, speech, and language processing, 2012, 20(1):14-22.
[75] PAN Jia, LIU Cong, WANG Zhiguo, et al. Investigation of deep neural networks (DNN) for large vocabulary continuous speech recognition:why DNN surpasses GMMS in acoustic modeling[C]//Proceedings of the 8th International Symposium on Chinese Spoken Language Processing (ISCSLP). Kowloon:IEEE, 2012:301-305.
[76] Microsoft. Microsoft audio video indexing service[EB/OL].[2016-11-11]. https://www.microsoft.com/en-us/research/project/mavis/.
[77] SEIDE F, LI Gang, YU Dong. Conversational speech transcription using context-dependent deep neural networks[C]//INTERSPEECH 2011, Conference of the International Speech Communication Association. Florence, Italy, 2011.
[78] MORIN F, BENGIO Y. Hierarchical probabilistic neural network language model[C]//Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics. Society for Artificial Intelligence and Statistics, 2005.
[79] COLLOBERT R, WESTON J. A unified architecture for natural language processing:deep neural networks with multitask learning[C]//Proceedings of the 25th International Conference on Machine Learning (ICML). NEC Laboratories America, Inc, 2008.
[80] MNIH A, HINTON G. A scalable hierarchical distributed language model[C]. Proceedings of the Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2008.
[81] MIKOLOV T, KOMBRINK S, ?ERNOCKI J, et al. Extensions of recurrent neural network language model[C]//Proceedings of 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague:IEEE, 2011.
[82] MIKOLOV T, DEORAS A, POVEY D, et al. Strategies for training large scale neural network language models[C]//Proceedings of 2011 IEEE Workshop on Automatic Speech Recognition and Understanding. Waikoloa, HI:IEEE, 2011.
[83] MIKOLOV T, ZWEIG G. Context dependent recurrent neural network language model[C]//Proceedings of 2012 IEEE Spoken Language Technology Workshop (SLT). Miami, FL:IEEE, 2012.
[84] MIKOLOV T, KARAFIÁT M, BURGET L, et al. Recurrent neural network based language model[C]//Proceedings of the INTERSPEECH 2010, 11th Conference of the International Speech Communication Association. Makuhari, Chiba, Japan, 2010.
[85] HUANG E H, SOCHER R, MANNING C D, et al. Improving word representations via global context and multiple word prototypes[C]//Proceedings of the Meeting of the Association for Computational Linguistics:Long Papers, F, 2012.
[86] MIKOLOV T, CHEN K, CORRADO G, et al. Efficient estimation of word representations in vector space[J]. Computer science, 2013,
[87] BAEZA-YATES R A, RIBEIRO-NETO B. Modern information retrieval:the concepts and technology behind search[M]. 2nd ed. New York:Addison Wesley,2011:26-28.
[89] HARRINGTON P. Machine learning in action[M]. Shelter Island, N.Y.:Manning Publications Co, 2012.
[90] 郑胤, 陈权崎, 章毓晋. 深度学习及其在目标和行为识别中的新进展[J]. 中国图象图形学报, 2014, 19(2):175-184. ZHENG Yin, CHEN Quanqi, ZHANG Yujin. Deep learning and its new progress in object and behavior recognition[J]. Journal of image and graphics, 2014, 19(2):175-184.
[91] CHEN Xuewen, LIN Xiaotong. Big data deep learning:challenges and perspectives[J]. IEEE access, 2014, 2:514-525.
相似文献/References:
[1]叶志飞,文益民,吕宝粮.不平衡分类问题研究综述[J].智能系统学报,2009,4(2):148.
 YE Zhi-fei,WEN Yi-min,LU Bao-liang.A survey of imbalanced pattern classification problems[J].CAAI Transactions on Intelligent Systems,2009,4(6):148.
[2]刘奕群,张 敏,马少平.基于非内容信息的网络关键资源有效定位[J].智能系统学报,2007,2(1):45.
 LIU Yi-qun,ZHANG Min,MA Shao-ping.Web key resource page selection based on non-content inf o rmation[J].CAAI Transactions on Intelligent Systems,2007,2(6):45.
[3]马世龙,眭跃飞,许 可.优先归纳逻辑程序的极限行为[J].智能系统学报,2007,2(4):9.
 MA Shi-long,SUI Yue-fei,XU Ke.Limit behavior of prioritized inductive logic programs[J].CAAI Transactions on Intelligent Systems,2007,2(6):9.
[4]姚伏天,钱沄涛.高斯过程及其在高光谱图像分类中的应用[J].智能系统学报,2011,6(5):396.
 YAO Futian,QIAN Yuntao.Gaussian process and its applications in hyperspectral image classification[J].CAAI Transactions on Intelligent Systems,2011,6(6):396.
[5]文益民,强保华,范志刚.概念漂移数据流分类研究综述[J].智能系统学报,2013,8(2):95.[doi:10.3969/j.issn.1673-4785.201208012]
 WEN Yimin,QIANG Baohua,FAN Zhigang.A survey of the classification of data streams with concept drift[J].CAAI Transactions on Intelligent Systems,2013,8(6):95.[doi:10.3969/j.issn.1673-4785.201208012]
[6]杨成东,邓廷权.综合属性选择和删除的属性约简方法[J].智能系统学报,2013,8(2):183.[doi:10.3969/j.issn.1673-4785.201209056]
 YANG Chengdong,DENG Tingquan.An approach to attribute reduction combining attribute selection and deletion[J].CAAI Transactions on Intelligent Systems,2013,8(6):183.[doi:10.3969/j.issn.1673-4785.201209056]
[7]胡小生,钟勇.基于加权聚类质心的SVM不平衡分类方法[J].智能系统学报,2013,8(3):261.
 HU Xiaosheng,ZHONG Yong.Support vector machine imbalanced data classification based on weighted clustering centroid[J].CAAI Transactions on Intelligent Systems,2013,8(6):261.
[8]辛雨璇,闫子飞.基于手绘草图的图像检索技术研究进展[J].智能系统学报,2015,10(2):167.[doi:10.3969/j.issn.1673-4785.201401045]
 XIN Yuxuan,YAN Zifei.Research progress of image retrieval based on hand-drawn sketches[J].CAAI Transactions on Intelligent Systems,2015,10(6):167.[doi:10.3969/j.issn.1673-4785.201401045]
[9]丁科,谭营.GPU通用计算及其在计算智能领域的应用[J].智能系统学报,2015,10(1):1.[doi:10.3969/j.issn.1673-4785.201403072]
 DING Ke,TAN Ying.A review on general purpose computing on GPUs and its applications in computational intelligence[J].CAAI Transactions on Intelligent Systems,2015,10(6):1.[doi:10.3969/j.issn.1673-4785.201403072]
[10]孔庆超,毛文吉,张育浩.社交网站中用户评论行为预测[J].智能系统学报,2015,10(3):349.[doi:10.3969/j.issn.1673-4785.201403019]
 KONG Qingchao,MAO Wenji,ZHANG Yuhao.User comment behavior prediction in social networking sites[J].CAAI Transactions on Intelligent Systems,2015,10(6):349.[doi:10.3969/j.issn.1673-4785.201403019]
[11]杨正理,史文,陈海霞,等.大数据背景下高校招生策略预测[J].智能系统学报,2019,14(2):323.[doi:10.11992/tis.201709011]
 YANG Zhengli,SHI Wen,CHEN Haixia,et al.The strategy of college enrollment predicted with big data[J].CAAI Transactions on Intelligent Systems,2019,14(6):323.[doi:10.11992/tis.201709011]

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
Copyright @ 《 智能系统学报》 编辑部
地址:(150001)黑龙江省哈尔滨市南岗区南通大街145-1号楼 电话:0451- 82534001、82518134