期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于平均期望间隔的多标签分类主动学习方法 被引量：1

Active Learning Method for Multi-label Classification Based on Average Expectation Margin
下载PDF
导出
摘要 针对多标签主动学习速度较慢的问题,提出一种基于平均期望间隔的多标签分类的主动学习方法。计算支持向量机分类器中的期望间隔,并将其作为样本选择标准。实验结果表明,该方法在分类精度、Hamming Loss、Coverage等评价标准上优于基于决策值和后验概率等主动学习策略,能更好地评价未标记样本,有效提高分类精度和速度。 Aiming at the problems that active learning in multi-label classification is slowly, this paper proposes an improved method for multi-label classification which based on average expectation margin. The method by calculating Support Vector Maehine（SVM） expectation margin as the selection criteria. Experimental results show that method proposed in this paper outperforms than other active learning strategy based on decision value and posterior probability strategy in terms of classification accuracy or Hamming Loss or Coverage. It can evaluate the unlabeled sample more appropriate, increase the classification accuracy and classification rate more efficient.
作者 刘端阳 邱卫杰
机构地区 浙江工业大学计算机科学与技术学院
出处 《计算机工程》 CAS CSCD 北大核心 2011年第15期168-170,共3页 Computer Engineering
关键词 多标签 后验概率 期望间隔 主动学习 支持向量机 multi-label posterior probability expectation margin active learning Support Vector Machine（SVM）
分类号 TP18 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献9

  • 1Yang Bisan, Tao Jiao, Wang Tengjiao, et al. Effective Multi-label Active Learning for Text Classification[C] //Proc. of KDD’09. Paris, France: ACM Press, 2009: 917-926.
  • 2樊继伟,李朝锋,吴小俊.一种改进的主动支持向量机算法及其应用[J].计算机工程,2009,35(19):178-180. 被引量：7
  • 3Li Xuchun, Wang Lei, Sung E. Multi-label SVM Active Learning for Image Classification[C] //Proc. of International Conference on Image Processing. Lion, France: [s. n.] , 2004: 2207-2210.
  • 4Brinker K. On Active Learning in Multi-label Classification[M] // Spiliopoulou M, Kruse R, Borgelt C, et al. From Data and Information Analysis to Knowledge Engineering. Heidelberg, Berlin, Germany: Springer, 2006: 206-213.
  • 5Lin Hsuan-Tien, Lin Chih-Jen, Weng R C. A Note on Platt’s Probabilistic Outputs for Support Vector Machines[J]. Journal of Machine Learning Research, 2007, 68(3): 267-276.
  • 6Tong S, Koller D. Support Vector Machine Active Learning with Applications to Text Classification[J]. Journal of Machine Learning Research, 2001, 2: 45-66.
  • 7He Jingrui, Li Mingjing, Zhang Hongjiang, et al. Mean Version Space: A New Active Learning Method for Content-based Image Retrieval[C] //Proc. of ACM MIR’04. New York, USA: ACM Press, 2004: 15-22.
  • 8Chang Chih-Chung, Lin Chi-Jen. LIBSVM: A Library for Support Vector Machine[EB/OL]. (2010-01-10). http://www.csie.ntu.tw/- cjlin/libsvm.
  • 9葛雷,李国正,尤鸣宇.多标记学习的嵌入式特征选择[J].南京大学学报（自然科学版）,2009,45(5):671-676. 被引量：12

二级参考文献20

  • 1李丹,李国正,陆文聪.用于药物活性预报的Co-Training方法[J].计算机科学,2006,33(12):159-161. 被引量：3
  • 2Vapnik. The Nature of Statistical Learning Theory[M]. New York, USA: [s. n.], 1998.
  • 3Simon T. Support Vector Machine Active Learning with Applications to Text Classification[J]. Journal of Machine Learning Research, 2002, 3(2): 45-66.
  • 4Darken C. Fast Adaptive K-means Clustering: Some Empirical Results[C]//Proc. of the International Joint Conference on Neural Networks. San Diego, USA: IEEE Press, 1990.
  • 5Schapire R E, Singer Y. Boostexter: A boosting-based system for text categorization. Machine Learning, 2000, 39 (2--3):135-168.
  • 6McCallum A. Multi-label text classification with a mixture model trained by EM. Working Notes of the AAAI' 99 Workshop on Text Learning. Orlando: AAAI, 1999.
  • 7Boutell M R, Luo J, Shen X, et al. Learning multi-label scene classification. Pattern Recognition, 2004, 37(9): 1757-1771.
  • 8Yin Z, Zhou Z H. Multi-label dimensionality reduction via dependency maximization. Proceedings of the 23^rd AAAI Conference on Artificial Intelligence, Chicago, IL: AAAI, 2008, 1503-1505.
  • 9Yu K, Yu S P, Tresp V. Multi-label informed latent semantic indexing. Proceedings of the 28^th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY:ACM, 2005, 258--265.
  • 10Moody J, Utans J. Principled architecture selection for neural networks: Application to corporate bond rating prediction. Moody J E, Hanson S J, Lippmann R P. Neural Information Processing Systems 4. Morgan Kaufmann Publishers, Inc. 1992, 683-690.

共引文献17

同被引文献18

  • 1Schapire R E, Singer Y. BoosTexter: a boosting-based system for text categorization [J]. Machine Learning, 2000, 39(2-3): 135-168. [DOI: 10.1023/A:1007649029923].
  • 2Hoi S C H, Jin R, Zhu J K, et al. Semi-supervised svm batch mode active learning for image retrieval [C]//Proceedings of the IEEE Computer Society Conference on Computer Vision and Patt-ern Recognition. Anchorage, AK: IEEE, 2008: 1-7. [DOI:10.1109/CVPR.2008.4587350].
  • 3Huang S J, Jin R, Zhou Z H. Active learning by querying infor-mative and representative examples [C]//Proceedings of the 24th Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: NIPS, 2010: 892-900. [DOI:10.1109/TPAMI.2014.2307881].
  • 4Balcan M F, Broder A Z, Zhang T. Margin based active learning [C]//Proceedings of the 20th Annual Conference on Learning Theory. San Diego, CA, USA: Springer Berlin Heidelberg, 2007: 35-50.
  • 5Freund Y, Seung H S, Shamir E, et al. Selective sampling using the query by committee algorithm [J]. Journal of Machine Learning, 1997, 28(2-3): 133-168. [DOI: 10.1023/A:1007330508534].
  • 6Yu K, Bi J, Tresp V. Active learning via transductive experimental design [C]//Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh, Pennsylvania, USA: ACM, 2006: 1081-1088. [DOI: 10.1145/1143844.1143980].
  • 7Dasgupta S, Hsu D. Hierachical sampling for active learning [C]//Proceedings of the 25th International Conference on Machine Learning. New York, USA: ACM, 2008: 208-215.
  • 8Donmwz P, Carbonell J G, Bennett P N. Dual strategy active learning [C]//Proceedings of the 18th European Conference on Machine Learning. Berlin: Springer, 2007: 116-127. [DOI: 10.1007/978-3-540-74958-5_14].
  • 9Nguyen H T, Smeulder A W. Active learning using pre-clustering [C]//Proceedings of the 21st International Conference on Machine Learning. New York, USA: ACM, 2004: 623-630.
  • 10Li X C, Wang L, Sung E. Multi-label SVM active learning for image Classification [C]//Proceedings of IEEE International Conference on Image Processing. Singapore: University of Nanyang Technology, 2004: 2207-2210. [DOI: 10.1109/ICIP. 2004. 1421535].

引证文献1

二级引证文献5

计算机工程
2011年 第15期

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部