Key instance detection in multi-instance learning

Journal of Machine Learning Research

Volumn 25, Issue , 2012, Pages 253-268

  Liu, Guoqing ^a   Wu, Jianxin ^a   Zhou, Zhi Hua ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b NANJING UNIVERSITY   (China)

Author keywords

Iterative rejection; Key instance detection; Multi instance learning; Neighborhood relation; Voting framework

Indexed keywords

ITERATIVE REJECTION; K-NN GRAPHS; LABEL PREDICTIONS; MULTI-INSTANCE LEARNING; NEAREST NEIGHBORS; NEIGHBORHOOD RELATION; REAL APPLICATIONS; VOTING FRAMEWORK;

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

LEARNING SYSTEMS;

EID: 84876818045     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (20)

1. S. Andrews, I. Tsochantaridis, and T. Hofmann. Support vector machines for multipleinstance learning. In Advances in Neural Information Processing Systems 15, pages 557- 584, Cambridge, MA, 2003. MIT Press.
2. I. Borg and P. Groenen. Modern Multidimensional Scaling: theory and applications. Springer-Verlag, New York, 2005.
3. Y. Chen, J. Bi, and J. Z. Wang. MILES: Multiple-instance learning via embedded instance selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12): 1931-1947, 2006.
4. J. Davis and M. Goadrich. The relationship between Precision-Recall and ROC curves. In the 23rd International Conference on Machine Learning, pages 233-240, Cambridge, MA, 2006. MIT Press.
5. G. Fung, M. Dundar, B. Krishnappuram, and R. B. Rao. Multiple instance learning for computer aided diagnosis. In Advances in Neural Information Processing Systems 19, pages 425-432, Cambridge, MA, 2007. MIT Press.
6. H. He. Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9):1263-1284, 2009.
7. T. Jebara, J. Wang, and S.-F. Chang. Graph construction and b-matching for semisupervised learning. In the 26th International Conference on Machine Learning, pages 441-448, 2009.
8. Y.-F. Li, J.T. Kwok, I.W. Tsang, and Z.-H. Zhou. A convex method for locating regions of interest with multi-instance learning. In the 20th European Conference Machine Learning, pages 15-30, 2009.
9. M. Maier and U. Luxburg. Inuence of graph construction on graph-based clustering measures. In Advances in Neural Information Processing Systems 20, pages 1025-1032, Cambridge, MA, 2009. MIT Press.
10. O. Maron and T. Lozano-Pérez. A framework for multiple-instance learning. In Advances in Neural Information Processing Systems 10, pages 570-576, Cambridge, MA, 1998. MIT Press.
11. A. Oliva and A. Torralba. Modeling the shape of the scene: A holistic representation of the spatial envelope. International Journal of Computer Vision, 42:145-175, 2001.
12. B. Settles, M. Craven, and S. Ray. Multiple instance active learning. In Advances in Neural Information Processing Systems 20, pages 1289-1296, Cambridge, MA, 2008. MIT Press.
13. D. Wang, J. Li, and B. Zhang. Multiple-instance learning via random walk. In the 17th European Conference Machine Learningg, pages 473-484, 2006.
14. J.Wang and J.-D. Zucker. Solving the multiple-instance problem: A lazy learning approach. In the 17th International Conference on Machine Learning, pages 1119-1125, 2000.
15. J. Wu and J.M. Rehg. CENTRIST: A visual descriptor for scene categorization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8):1489-1501, 2011.
16. Q. Zhang and S. Goldman. EM-DD: An improved multiple-instance learning technique. In Advances in Neural Information Processing Systems 14, pages 1073-1080, Cambridge, MA, 2002. MIT Press.
17. D. Zhou, B. Schölkopf, and T. Hofmann. Semi-supervised learning on directed graphs. In Advances in Neural Information Processing Systems 17, pages 1633-1640, Cambridge, MA, 2005a. MIT Press.
18. Z.-H. Zhou, X.-B. Xue, and Y. Jiang. Locating regions of interest in cbir with multiinstance learning techniques. In the 18th Australian Joint Conference on Artificial Intelligence, pages 92-101, 2005b.
19. Z.-H. Zhou, Y.-Y. Sun, and Y.-F. Li. Multi-instance learning by treating instances as non-i.i.d. samples. In the 26th International Conference on Machine Learning, pages 1249-1256, 2009.
20. X. Zhu. Semi-supervised learning tutorial. Technical report, Department of Computer Sciences University of Wisconsin, Madison, USA, 2007.