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基于多样性密度的多示例学习方法 被引量：3

Multi-instance Learning Algorithm Based on Diverse Density
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摘要 结合多样性密度和带负类的支持向量数据描述,提出了一种能够有效解决多示例问题的算法:MIL-NSVDD_DD。该算法首先通过多样性密度算法找出多示例问题中最优示例模型,然后通过使用带负类的支持向量数据描述对示例模型进行训练,以得到最终的分类器,用得到的分类器再对新包进行预测。最后通过实验表明了该算法的有效性。 In this paper,based on diverse density and support vector data description,we proposed MIL-NSVDD_DD algorithm which can solve multi-instance learning problem effectively.The algorithm firstly through diverse density method to find some optimal instance prototypes,secondly train these instance prototypes by Negative-SVDD can get a classifier,then use the classifier to predict new bag,finally the experimental results are promising.
作者 龙哲
机构地区 杭州电子科技大学图形图像研究所
出处 《工业控制计算机》 2012年第7期73-74,80,共3页 Industrial Control Computer
关键词 多示例学习 多样性密度 支持向量数据描述 机器学习 multiple-instance learning diverse density support vector data description machine learning
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献7

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  • 2O.Maron and T.Lozano- perez, A framework for multiple-in- stance learning. In: M.l.Jordan, M.J.Kearns and S.A.Solla eds., Advances in Neural Information Processing Systems 10, Cambridge, MA: MIT Press, pp.570-576, 1998.
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同被引文献27

  • 1戴宏斌,张敏灵,周志华.一种基于多示例学习的图像检索方法[J].模式识别与人工智能,2006,19(2):179-185. 被引量：9
  • 2Dietterich T G, Lathrop R H, Lozano P T.Solving the multiple-instance problem with axisparallel rectangles[J]. Artificial Intelligence, 1997,89 ( 1/2 ) : 31-71.
  • 3Maron O, Ratan A L.Multiple-instance learning for natural scene classification[C]//Proceedings of ICML, Madison, USA, 1998 : 341-349.
  • 4Ruffo G.Learning single and multiple instance decision tree for computer security applications[D].Torino: University of Turin, 2000.
  • 5Andrews S,Hofman T,Tsochantaridis 1.Multiple instance learning with generalized support vector machines[C]// Proceedings of AAA1/IAA1, Edmonton, Canada, 2002: 943 -944.
  • 6Huang X, Chen S C, Shy M L, et al.User concept pattern discovery using relevance feedback and multiple instancelearning for content-based image retrieval[C]//Proceedings of MDM/KDD2002 Workshop, Edmomton, Canada, 2002 : 100-108.
  • 7Yang C, Lozano P T.Image database retrieval with multiple- instance learning techniques[C]//Proceedings of ICDE, San Diego, USA, 2000 : 233-243.
  • 8Zhang Q, Goldman S A, Yu W, et al.Content-based image retirveal using multiple-instance learning[C]//Proceedings of ICML, Sydney,Australian,2002 : 682-689.
  • 9Chevaleyre Y,Zucker J D.Solving multiple-instance and multiple-part learning problems with decision trees and decision rules:application to the mutagenesis problem[C]// Proceedings of LNAI, Berlin, German, 2001 : 204-214.
  • 10Maron O, Ratan A L.A framework for multiple-instance learning[C]//Proceedings of NIPS, Cambridge, USA, 1998 : 570-576.

引证文献3

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工业控制计算机
2012年 第7期

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