[1] 刘玉超.基于云模型的粒计算方法研究.博士学位论文.北京:清华大学, 2012.
(LIU Y C. Research on Particle Computation Method Based on Cloud Model. Ph.D Dissertation. Beijing, China: Tsinghua University, 2012.)
[2] 李德毅,刘常昱,杜 鹢,等.不确定性人工智能.软件学报, 2004, 15(11): 1583-1594.
(LI D Y, LIU C Y, DU Y, et al. Artificial Intelligence with Uncertainty. Journal of Software, 2004, 15(11): 1583-1594.)
[3] 吴信东,何 进,陆汝钤,等.从大数据到大知识: HACE+BigKE.自动化学报, 2016, 42(7): 965-983.
(WU X D, HE J, LU R Q, et al. From Big Data to Big Know-ledge: HACE+BigKE. Acta Automatica Sinica, 2016, 42(7): 965-983.)
[4] WU X D, ZHU X Q, WU G Q, et al. Data Mining with Big Data. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(1): 97-107.
[5] MARX V. Biology: The Big Challenges of Big Data. Nature, 2013, 498(7453): 255-260.
[6] CLIFFORD L. Big Data: How Do Your Data Grow? Nature, 2008, 455(7209): 28-29.
[7] 杨 洁,王国胤,刘 群,等.正态云模型研究回顾与展望.计算机学报, 2018, 41(3): 724-744.
(YANG J, WANG G Y, LIU Q, et al. Retrospect and Prospect of Research of Normal Cloud Model. Chinese Journal of Computers, 2018, 41(3): 724-744.)
[8] LUSH G J. Probability Theory. Nature, 1978, 272(5648): 107.
[9] ZADEH L A. Fuzzy Sets. Information and Control, 1965, 8(3): 338-353.
[10] PAWLAK Z. Rough Sets. International Journal of Computer and Information Sciences, 1982, 11(5): 341-356.
[11] YAGER R R. On the Dempster-Shafer Framework and New Combination Rules. Information Sciences, 1987, 41(2): 93-137.
[12] 李德毅,孟海军,史雪梅.隶属云和隶属云发生器.计算机研究与发展, 1995, 32(6): 15-20.
(LI D Y, MENG H J, SHI X M. Membership Clouds and Membership Cloud Generators. Journal of Computer Research and Development, 1995, 23(6): 15-20.)
[13] 王国胤,李德毅,姚一豫,等.云模型与粒计算.北京:科学出版社, 2012.
(WANG G Y, LI D Y, YAO Y Y, et al. Cloud Model and Granular Computing. Beijing, China: Science Press, 2012.)
[14] DENG W H, WANG G Y, XU J. Piecewise Two-Dimensional Normal Cloud Representation for Time-Series Data Mining. Information Sciences, 2016, 374: 32-50.
[15] 李海林,郭崇慧,邱望仁.正态云模型相似度计算方法.电子学报, 2011, 39(11): 2561-2567.
(LI H L, GUO C H, QIU W R. Similarity Measurement between Normal Cloud Models. Acta Electronica Sinica, 2011, 39(11): 2561-2567.)
[16] 查 翔,倪世宏,谢 川,等.云相似度的概念跃升间接计算方法.系统工程与电子技术, 2015, 37(7): 1676-1682.
(ZHA X, NI S H, XIE C, et al. Indirect Computation Approach of Cloud Model Similarity Based on Conception Skipping. Systems Engineering and Electronics, 2015, 37(7): 1676-1682.)
[17] 许昌林,王国胤.正态云概念的漂移性度量及分析.计算机科学, 2014, 41(7): 9-14, 51.
(XU C L, WANG G Y. Excursive Measurement and Analysis of Normal Cloud Concept. Computer Science, 2014, 41(7): 9-14, 51.)
[18] 李德毅.知识表示中的不确定性.中国工程科学, 2000, 2(10): 73-79.
(LI D Y. Uncertainty in Knowledge Representation. Engineering Science, 2000, 2(10): 73-79.)
[19] 李德毅,刘常昱,淦文燕.正态云模型的重尾性质证明.中国工程科学, 2011, 13(4): 20-23.
(LI D Y, LIU C Y, GAN W Y. Proof of the Heavy-Tailed Property of Normal Cloud Model. Engineering Science, 2011, 13(4): 20-23.)
[20] 李德毅,杜 鹢.不确定性人工智能.北京:国防工业出版社, 2005.
(LI D Y, DU Y. Artificial Intelligence with Uncertainty. Beijing, China: National Defense Industry Press, 2005.)
[21] YAO Y Y. Granular Computing: Past, Present, and Future // Proc of the International Conference on Rough Sets and Knowledge Technology. Berlin, Germany: Springer, 2008: 27-28.
[22] 许昌林.基于云模型的双向认知计算方法研究.博士学位论文.成都:西南交通大学, 2014.
(XU C L. Method of Bidirectional Cognitive Computing Based on Cloud Model. Ph.D Dissertation. Chengdu, China: Southwest Jiaotong University, 2014.)
[23] LIU Y C, LI D Y, HE W, et al. Granular Computing Based on Gaussian Cloud Transformation. Fundamenta Informaticae, 2013, 127(1/2/3/4): 385-398.
[24] 姚 红,王国胤,张清华.基于粗糙集和云模型的彩色图像分割方法.小型微型计算机系统, 2013, 34(11): 2615-2620.
(YAO H, WANG G Y, ZHANG Q H. Color Image Segmentation Method Based on Rough Set and Cloud Model. Journal of Chinese Computer Systems, 2013, 34(11): 2615-2620.)
[25] 徐 计,王国胤,于 洪.基于粒计算的大数据处理.计算机学报, 2015, 38(8): 1497-1517.
(XU J, WANG G Y, YU H. Reviews of Big Data Processing Based on Granular Computing. Chinese Journal of Computers, 2015, 38(8): 1497-1517.)
[26] RUBNER Y, TOMASI C, GUIBAS L J. The Earth Mover′s Distance as a Metric for Image Retrieval. International Journal of Computer Vision, 2000, 40(2): 99-121.
[27] REN Z, YUAN J S, MENG J J, et al. Robust Part-Based Hand Gesture Recognition Using Kinect Sensor. IEEE Transactions on Multimedia, 2013, 15(5): 1110-1120.
[28] RABIN J, DELON J, GOUSSEAU Y. Circular Earth Mover′s Distance for the Comparison of Local Features // Proc of the 19th International Conference on Pattern Recognition. Washington, USA: IEEE, 2008. DOI: 10.1109/ICPR.2008.4761372.
[29] LING H B, OKADA K. An Efficient Earth Mover′s Distance Algorithm for Robust Histogram Comparison. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(5): 840-853.
[30] ZHANG M H, PENG J H, LIU X J. Sparse Coding with Earth Mover′s Distance for Multi-instance Histogram Representation. Neural Computing and Applications, 2017, 28(12): 3697-3708.
[31] QIAN Y H, LIANG J Y, DANG C Y. Knowledge Structure, Knowledge Granulation and Knowledge Distance in a Knowledge Base. International Journal of Approximate Reasoning, 2009, 50(1): 174-188.
[32] YAO Y Y. Information Granulation and Rough Set Approximation. International Journal of Intelligent Systems, 2001, 16(1): 87-104.
[33] 张燕平,张 铃,吴 涛.不同粒度世界的描述法——商空间法.计算机学报, 2004, 27(3): 328-333.
(ZHANG Y P, ZHANG L, WU T. The Representation of Different Granular Worlds: A Quotient Space. Chinese Journal of Computers, 2004, 27(3): 328-333.)
[34] ZHANG B, ZHANG L. Theory and Applications of Problem So-lving. New York, USA: Elsevier Science, 1992.
[35] QIAN Y H, CHENG H H, WANG J J, et al. Grouping Granular Structures in Human Granulation Intelligence. Information Sciences, 2017, 382/383: 150-169.
[36] LEVINA E, BICKEL P. The Earth Mover′s Distance is the Ma-llows Distance: Some Insights from Statistics // Proc of the 8th IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2001, II: 251-256.
[37] KULLBACK S, LEIBLER R A. On Information and Sufficiency. The Annals of Mathematical Statistics, 1951, 22(1): 79-86.
[38] KARACAN L, ERDEM A, ERDEM E. Image Matting with KL-Divergence Based Sparse Sampling // Proc of the IEEE International Conference on Computer Vision. Washington, USA: IEEE, 2015: 424-432.
[39] WANG B Y, ZHANG S M. A Novel Text Classification Algorithm Based on Naïve Bayes and KL-Divergence // Proc of the International Conference on Parallel and Distributed Computing, Applications and Technologies. Washington, USA: IEEE, 2006: 913-915.
[40] CONTRERAS-REYES J E, ARELLANO-VALLE R B. Kullback-Leibler Divergence Measure for Multivariate Skew-Normal Distributions. Entropy, 2012, 14(9): 1606-1626.
[41] PATIL S, RAJWADE A. Reconstruction Error Bounds for Compressed Sensing under Poisson Noise Using the Square Root of the Jensen-Shannon Divergence[C/OL]. [2018-02-12]. https://arxiv.org/pdf/1606.08557v1.pdf.
[42] JEFFREYS H. An Invariant Form for the Prior Probability in Estimation Problems. Proceedings of the Royal Society of London, 1946, 186(1007): 453-461.
[43] HULLERMEIER E, RIFQI M, HENZGEN S, et al. Comparing Fuzzy Partitions: A Generalization of the Rand Index and Related Measures. IEEE Transactions on Fuzzy Systems, 2012, 20(3): 546-556. |