期刊文献+

一种基于条件随机场(CRF)的运动轨迹填补方法

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摘要 关于运动轨迹填补方法,以前的研究只考虑了数据之间的局部关联性而忽视了全局关联性,条件随机场方法可以有效解决这个问题。首先利用基于用户的协同过滤算法寻找候选集,再利用基于距离的聚类算法对候选集预处理,达到筛选的目的;最后利用条件随机场理论对运动轨迹的缺失值进行填补。实验表明:该方法准确度较高。
作者 王宗时
出处 《软件导刊》 2016年第2期12-14,共3页 Software Guide
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参考文献8

  • 1DEMPSTER A P, LAIRD N M, RUBIN D B. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society, Series B ( Methodological), 1977 (3) : 1-38.
  • 2LAKSHMINARAYAN K, HARP S A,GOLDMAN R, et al. Impu ration of missing data using machine learning techniques[C]. Pro ceedings of the Second International Conference on Knowledge Dis covery and Data Mining, 1996 : 140-145.
  • 3张婵.一种基于支持向量机的缺失值填补算法[J].计算机应用与软件,2013,30(5):226-228. 被引量:15
  • 4于力超,金勇进,王俊.缺失数据插补方法探讨——基于最近邻插补法和关联规则法[J].统计与信息论坛,2015,30(1):35-40. 被引量:22
  • 5JOHN LAFFERTY, ANDREW MCCALLUM, FERNANDO PEREI- RA. Conditional random fields: probabilistic models for segmenting and labeling sequence data[C]. Proc of the 8th International Con- ference on Machine Learning, 2002 : 282-291.
  • 6PREM MELVILI.E, RAYMOND J MOONEY, RAMADASS NAGARAJAN. Content-boosted collaborative filtering for improved recommendations [EB/OL]. http://www, doc88, com/p - 9733190151056. html.
  • 7A KOHRS,B MERIALDO. Creating user-adapted websites by the use of collaborative filtering[J]. Interacting with Computers, 2001, 13(6) : 695-716.
  • 8JAIN AK,DUBES RC. Algorithms for clustering data[J]. Prentice -Hall Advanced Reference Series, 1988(5) :1-334.

二级参考文献16

  • 1Witten I H,Frank E,Hall M A.Data Mining:Practical machinelearning tools and techniques[M].Morgan Kaufmann,2011.
  • 2Han J W,Kamber M.Data Mining Concepts and Techniques[M].范明,译.2版.北京:机械工业出版社,2001:257-259.
  • 3Cortes C,Vapnik V.Support vector networks[J].Machine Learning,1995,20:273-297.
  • 4Blake C,Keogh E,Merz CJ.UCI repository of machine learning data-bases[EB/OL].Department of Information and Computer Science,U-niversity of California,Irvine,CA,1998.http://www.ics.uci.edu/~mlearn/MLRepository.html.
  • 5LeCun Y,Jackel L D,Bottou L,et al.Comparison of learning algo-rithms for handwritten digit recognition[C]//F Fogelman,P Galli-nari.Proc.Int’l Conf.Artifcial Neural Network:53-60.
  • 6Quinlan J R.Induction of decision trees[J].Machine learning,1986,1(1):81-106.
  • 7Vapnik V.The Nature of Statistical Learning Theory[M].New York:Springer-Verlag,1995.
  • 8Zhou Zhi Hua,Jiang Yuan.Nec4.5:neural ensemble based C4.5[J].IEEE Transactions on Knowledge and Data Engineering,2004,16(6):770-773.
  • 9Kohavi R.A study of cross-validation and bootstrap for accuracy esti-mation and model selection[C]//Wermter S,Riloff E,Scheler G.Proc.14th Joint Int.Conf.Artificial Intelligence.San Mateo,CA:Morgan Kaufmann,1995:1137-1145.
  • 10Agrawal R, Imielinski T, Swami A. 1Vraning Association Rules between Sets of It:ns in Large Databases[C]. Proceedings of the ACM SIGMOD Conference on Management of Data, Washington, IX], USA, 1993.

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