A new SVM approach to multi-instance multi-label learning

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2010, Pages 384-392

  Nguyen, Nam ^a  

^a Department of Obstetrics Gynecology   (United States)

Author keywords

Classification; Multi instance multi label; SVM

Indexed keywords

CLASSIFICATION; DATA SETS; EFFICIENT METHOD; EMPIRICAL RISKS; GENE SEQUENCES; L2-NORM; MULTI-LABEL; MULTIPLE CLASS; MULTIPLE INSTANCES; OPTIMIZATION APPROACH; REAL-WORLD APPLICATION; REAL-WORLD TASK; SCENE CLASSIFICATION; STOCHASTIC GRADIENT; SVM; TEXT CATEGORIZATION;

CLASSIFICATION (OF INFORMATION); DATA MINING; GENE ENCODING; OPTIMIZATION; QUADRATIC PROGRAMMING;

TEXT PROCESSING;

EID: 79951766505     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2010.109     Document Type: Conference Paper

References (38)

1. Z.-H. Zhou and M.-L. Zhang, "Multi-instance multi-label learning with application to scene classification," in The Neural Information Processing Systems (NIPS), 2006, pp. 1609-1616.
2. M.-L. Zhang and Z.-H. Zhou, "M3MIML: A maximum margin method for multi-instance multi-label learning," in ICDM, 2008, pp. 688-697.
3. M.-L. Zhang and Z.-J. Wang, "MIMLRBF: RBF neural networks for multi-instance multi-label learning," Neurocomputing, vol. 72, no. 16-18, pp. 3951-3956, 2009.
4. Z. J. Zha, X. S. Hua, T. Mei, J. D. Wang, G. J. Qi, and Z. F. Wang, "Joint multi-label multi-instance learning for image classification," in CVPR, 2008, pp. 1-8.
5. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Perez, "Solving the multiple instance problem with axis-parallel rectangles," Artificial Intelligence, vol. 89, pp. 31-71, 1997. (Pubitemid 127412230)
6. A. McCallum, "Multi-label text cassification by EM," AAAI'99 Workshop on Text Learning., 1999.
7. R. E. Schapire and Y. Singer, "BOOSTEXTER: a boosting-based system for text categorization," Machine Learning, vol. 39, no. 2/3, pp. 135-168, 2000. (Pubitemid 30594821)
8. S. Andrews, I. Tsochantaridis, and T. Hofmann, "Support vector machines for multiple-instance learning," in The Neural Information Processing Systems (NIPS), 2002, pp. 561-568.
9. Y. Chevaleyre and J.-D. Zucker, "Solving multiple-instance and multiple-part learning problems with decision trees and rule sets. application to the mutagenesis problem," Lecture Notes in Computer Science, vol. 2056, pp. 204-214, 2001. (Pubitemid 33276101)
10. O. Maron and T. Lozano-Pérez, "A framework for multipleinstance learning," in The Neural Information Processing Systems (NIPS), 1997.
11. S. Andrews and T. Hofmann, "Multiple-instance learning via disjunctive programming boosting," in The Neural Information Processing Systems, 2003.
12. S. Ray and D. Page, "Multiple instance regression," in Proceedings of The International Conference on Machine Learning (ICML), 2001, pp. 425-432.
13. J. Wang, et Jean-Daniel Zucker, and J. daniel Zucker, "Solving the multiple-instance problem: A lazy learning approach," in Proceedings of The International Conference on Machine Learning (ICML), 2000, pp. 1119-1125.
14. Y. X. Chen, J. B. Bi, and J. Z. Wang, "MILES: Multipleinstance learning via embedded instance selection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 1931-1947, 2006. (Pubitemid 46405705)
15. Y. Chen and J. Z. Wang, "Image categorization by learning and reasoning with regions," JMLR, vol. 5, pp. 913-939, 2004.
16. Q. Zhang, S. A. Goldman, W. Yu, and J. E. Fritts, "Content-based image retrieval using multiple-instance learning," in Proceedings of The International Conference on Machine Learning (ICML), 2002, pp. 682-689.
17. O. Maron and A. L. Ratan, "Multiple-instance learning for natural scene classification," in Image Understanding Workshop, 1998, pp. 1031-1036.
18. Xu, Jinxi, Croft, and W. Bruce, "Query expansion using local and global document analysis," in Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ser. Experimental Studies, 1996, pp. 4-11.
19. J. Yang, "Review of multi-instance learning and its application," 2001. [Online]. Available: http://www.cs.cmu.edu/juny/MILL/ review.htm
20. M. R. Boutell, J. B. Luo, X. P. Shen, and C. M. Brown, "Learning multi-label scene classification," Pattern Recognition, vol. 37, no. 9, pp. 1757-1771, 2004. (Pubitemid 38804465)
21. S. Gao, W. Wu, C.-H. Lee, and T.-S. Chua, "A MFoM learning approach to robust multiclass multi-label text categorization," in Proceedings of The International Conference on Machine Learning (ICML), 2004.
22. K. Brinker, J. Fürnkranz, and E. Hüllermeier, "A unified model for multilabel classification and ranking," in UCAI, vol. 141, 2006, pp. 489-493.
23. A. Elisseeff and J. Weston, "A kernel method for multi-labelled classification," in The Neural Information Processing Systems (NIPS), 2001, pp. 681-687.
24. H. Kazawa, T. Izumitani, H. Taira, and E. Maeda, "Maximal margin labeling for multi-topic text categorization," in The Neural Information Processing Systems (NIPS), 2004.
25. M.-L. Zhang and Z.-H. Zhou, "Multilabel neural networks with applications to functional genomics and text categorization," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 10, pp. 1338-1351, 2006. (Pubitemid 44335199)
26. S. Zhu, X. Ji, W. Xu, and Y. Gong, "Multi-labelled classification using maximum entropy method," in Proceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2005, pp. 274-281.
27. N. Ueda and K. Saito, "Parametric mixture models for multilabeled text," in The Neural Information Processing Systems (NIPS), 2002, pp. 721-728.
28. G. Tsoumakas and I. Katakis, "Multi-label classification: An overview," IJDWM, vol. 3, no. 3, pp. 1-13, 2007.
29. X. Xu and E. Frank, "Logistic regression and boosting for labeled bags of instances," pp. 272-281, 2004.
30. G. A. Edgar, Measure, topology, and fractal geometry. New York: Springer, 1990.
31. M.-L. Zhang and Z.-H. Zhou, "Multi-instance clustering with applications to multi-instance prediction," Application Intell, vol. 31, no. 1, pp. 47-68, 2009.
32. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes: The Art of Scientific Computing. Cambridge University Press, 1986.
33. K. Crammer and Y. Singer, "On the algorithmic implementation of multiclass kernel-based vector machines," Journal of Machine Learning Research, vol. 2, pp. 265-292, 2001.
34. J. C. Bezdek and R. J. Hathaway, "Convergence of alternating optimization," Neural, Parallel & Scientific Computations, vol. 11, no. 4, pp. 351-368, 2003. (Pubitemid 37506735)
35. S. V. N. Vishwanathan, N. N. Schraudolph, M. W. Schmidt, and K. P. Murphy, "Accelerated training of conditional random fields with stochastic gradient methods," in Proceedings of The International Conference on Machine Learning (ICML), 2006, pp. 969-976.
36. S. Shalev-Shwartz, Y. Singer, and N. Srebro, "Pegasos: Primal estimated sub-gradient solver for svm," in Proceedings of the 24th International Conference on Machine Learning. New York, NY, USA: ACM, 2007, pp. 807-814.
37. A. Asuncion and D. Newman, "UCI machine learning repository," 2007. [Online]. Available: http://www.ics.uci.edu/~mlearn/MLRepository.html
38. F. Sebastiani, "Machine learning in automated text categorization," ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002.