Multi-instance multi-label learning for automatic tag recommendation

Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

Volumn , Issue , 2009, Pages 4910-4914

  Shen, Chen ^a,b   Jiao, Jun ^c   Yang, Yahui ^b   Wang, Bin ^a  

^a INSTITUTE OF COMPUTING TECHNOLOGY   (China)

^b PEKING UNIVERSITY   (China)

^c NANJING UNIVERSITY   (China)

Author keywords

Machine learning; Multi instance; Multi label; Tag recommendation

Indexed keywords

INTERNET SERVICES; LEARNING FRAMEWORKS; LEARNING METHODS; MACHINE-LEARNING; MULTI-LABEL; WEB PAGE;

CYBERNETICS; INTERNET; LABELS; LEARNING SYSTEMS; WORLD WIDE WEB;

LEARNING ALGORITHMS;

EID: 74949103746     PISSN: 1062922X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICSMC.2009.5346261     Document Type: Conference Paper

References (18)

1. G. Begelman, P.Keller, and F.Smadja. Automated tag clustering: Improving search and exploration in the tag space. In Collaborative Web Tagging Workshop at WWW2006, Edinburgh, Scotland, 2006.
2. P. A. Chirita, S. Costache, W. Nejdl, and S. Handschuh. P-tag: large scale automatic generation of personalized annotation tags for the web. In WWW 07: Proceedings of the 16th international conference on World Wide Web, pages 845C854, New York, NY, USA, 2007. ACM Press.
3. S. Golder and B. Huberman. Usage patterns of collaborative tagging systems. J. Inf. Sci., 2006.
4. Song Yang and Zhang Lu and Giles C. Lee,. A sparse gaussian processes classification framework for fast tag suggestions. CIKM '08: Proceeding of the 17th ACM conference on Information and knowledge management, New York, NY, USA, 2008.
5. G. Tsoumakas, I. Katakis, and I. Vlahavas.Effective and efficientmultilabel classification in domains with large number of labels. In Proc. ECML/PKDD 2008 Workshop on Mining Multidimensional Data
6. Zhou, Z.-H., and Zhang, M.-L. 2007. Multi-instance multi-label learning with application to scene classification. Proc. Advances in Neural Information Processing Systems
7. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Perez. Solving the multiple-instance problem with axisparallel rectangles. Artificial Intelligence, 89(1-2):31C71, 1997.
8. O. Maron and A. L. Ratan. Multiple-instance learning for natural scene classification. In Proceedings of the 15th International Conference on Machine Learning, pages 341C349, Madison, MI, 1998.
9. Z.-H. Zhou. Multi-instance learning: A survey. Technical report, AI Lab, Department of Computer Science & Technology, Nanjing University, Nanjing, China, 2004.
10. G. Tsoumakas and I. Katakis. Multi-label classification: an overview. International Journal of Data Warehousing and Mining, 3(3):1C13, 2007.
11. Doug Beeferman, Adam Berger, and John Lafferty. 1999. Statistical models for text segmentation. Machine Learning, 34(1-3):177C210.
12. Masao Utiyama, Hitoshi Isahara. A Statistical Model for Domain-Independent Text Segmentation. The 39th Annual Meeting of the Association for Computational Linguistics and 10th Conference of the European Chapter of the Association for Computational Linguistics. 2001. 491-498.
13. Marti A. Hearst. 1994. Multi-paragraph segmentation of expository text. In Proc. of ACL94.
14. Y. Song, Z. Zhuang, H. Li, Q. Zhao, J. Li, W. C. Lee, and C. L. Giles. Real-time automatic tagrecommendation. In SIGIR 08: Proceedings of the31st annual international ACM SIGIR conference onResearch and development in information retrieval, pages 515C522, New York, NY, USA, 2008. ACM.
15. Y. Yang and J. O. Pedersen. A comparative study on feature selection in text categorization. In Proceedings of the 14th International Conference on Machine Learning, pages 412C 420, Nashville, TN, 1997.
16. G. A. Edgar. Measure, Topology, and Fractal Geometry. Springer, Berlin, 1990.
17. G. Tsoumakas and I. Vlahavas. Random k-labelsets:An ensemble method for multilabel classification. In Proceedings of the 18th European Conference on Ma-chine Learning (ECML 2007), pages 406C417, Warsaw, Poland, September 17-21 2007.
18. Zhang, M.L., Zhou, Z.H.: A k-nearest neighbor based algorithm for multi-label classication. In Proceedings of the 1st IEEE International Conference on Gran- ular Computing. (2005) 718-721