|本期目录/Table of Contents|

[1]刘领全,许敏,刘德强.基于最小包含球的领域自适应算法[J].温州职业技术学院学报,2013,04:70-74.
 LIU Lingquan,XU Min,LIU Deqiang.Field Adaptive Algorithm based on Minimum Enclosing Ball[J].Journal of Wenzhou Vocational and Technical College,2013,04:70-74.
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基于最小包含球的领域自适应算法(PDF)

《温州职业技术学院学报》[ISSN:1006-6977/CN:61-1281/TN]

期数:
2013年04期
页码:
70-74
栏目:
应用技术
出版日期:
2013-12-15

文章信息/Info

Title:
Field Adaptive Algorithm based on Minimum Enclosing Ball
作者:
刘领全;许敏;刘德强
1.无锡职业技术学院 物联网技术学院,江苏 无锡 214121; 2.江南大学 数字媒体学院,江苏 无锡 214122
Author(s):
LIU Lingquan1 XU Min12 LIU Deqiang1
1.School of Internet of Things Technology, Wuxi Institute of Technology, Wuxi, 214121, China; 2.School of Digital Media, Southern Yangtze University, Wuxi, 214122, China
关键词:
领域自适应SVDD最小包含球数据集
Keywords:
Field adaption SVDD Minimum enclosing ball Data collection
分类号:
TP181
DOI:
-
文献标识码:
A
摘要:
相同应用领域因不同时间、地点或设备,检测到的数据域可能会出现不完全一致的现象,从而可 能导致机器学习效率降低。为有效地进行数据域间知识传递,在原有支持向量域描述(SVDD)算法的基础上,提 出一种全新的数据域中心点校正的领域自适应算法,并使用人造数据集和KDD CUP99 大数据集验证算法。实验证 明,该领域自适应算法效果较好,将其应用于大数据集可减少核心集元素个数,提高运算效率。
Abstract:
Due to different time, places or equipments, data domain detected by the same application field may not be consistent completely, which may lead to lowering in machine learning efficiency. To effectively transmit knowledge between data domains, a new field adaptive algorithm of date center correction is presented based on the original support vector domain description (SVDD) algorithm and the artificial data sets and KDD CUP99 large data collection validation algorithm are applied. It is shown that its application in the large data collection can reduce the number of elements in the core set and improve the efficiency.

参考文献/References

[1] Yang J,Yan R,Hauptmann A G.Cross-domain video concept detection using adaptive SVMs[C]//Proceedings of the 15th International Conference on Multimedia.Bavaria:Augsburg University Press,2007:188-197.
[2] Blitzer J,Mcdonald R,Pereira F.Domain adaptation with structural correspondence learning[C]//Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing.Sydney:NSW,2006:120-128.
[3] Pan S J,Tsang I W,Kwok J T,et al.Domain adaptation via transfer component analysis[J].Ieee Transactions on Neural Networks, 2011,22(2):199-210.
[4] Tax D,Duin R.Support vector domain description[J].Pattern Recognition Letters,1999,20(11/13):1191-1199.
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[6] Tsang I W,Kwok J T,Zurada J M.Generalized core vector machines[J].Ieee Transactions on Neural Networks,2006,17(5): 1126-1140.
[7] Badoiu M,Clarkson K L.Optimal core-sets for balls[J].Computational Geometry:Theory and Applications,2008,40(1):14-22.
[8] KDD CUP99 数据库[DB/OL].
[2013-09-01].http://kdd.ics.uci.edu/databases/kddcup99/task.html.

备注/Memo

备注/Memo:
[收稿日期]2013-09-12 [基金项目]江苏省高校大学生实践创新训练计划项目(2012JSSPITP3334);江苏省教育厅高校哲学社会科学研究基金(2012SJB880077) [作者简介]刘领全(1992 —),男,江苏泰州人,无锡职业技术学院物联网技术学院专科生; 许敏(1980 —),女,江苏无锡人,无锡职业技术学院物联网技术学院讲师,江南大学数字媒体学院博士研究生; 刘德强(1965 —),男,江苏靖江人,无锡职业技术学院物联网技术学院副教授.
更新日期/Last Update: 2013-12-20