Class-dependent dissimilarity measures for multiple instance learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7626 LNCS, Issue , 2012, Pages 602-610

  Cheplygina, Veronika ^a   Tax, David M J ^a   Loog, Marco ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

DISSIMILARITY MEASURES; FEATURE VECTORS; MULTIPLE-INSTANCE LEARNING;

PATTERN RECOGNITION; SYNTACTICS;

LEARNING SYSTEMS;

EID: 84868159293     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-34166-3_66     Document Type: Conference Paper

References (21)

1. Andrews, S., Hofmann, T., Tsochantaridis, I.: Multiple instance learning with generalized support vector machines. In: Proc. of the National Conference on Artificial Intelligence, pp. 943-944. AAAI Press, MIT Press, Menlo Park, Cambridge (2002)
2. Bailey, A.: Class-dependent features and multicategory classification. Ph.D. thesis, Citeseer (2001)
3. Chen, Y., Bi, J., Wang, J.: Miles: Multiple-instance learning via embedded instance selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 1931-1947 (2006)
4. Cheplygina, V., Tax, D., Loog, M.: Does one rotten apple spoil the whole barrel? In: International Conference on Pattern Recognition (in press)
5. De Wachter, M., Demuynck, K., Wambacq, P., Van Compernolle, D.: A locally weighted distance measure for example based speech recognition. In: International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. I-181. IEEE (2004)
6. Dietterich, T., Lathrop, R., Lozano-Perez, T.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89(1-2), 31-71 (1997)
7. Gartner, T., Flach, P., Kowalczyk, A., Smola, A.: Multi-instance kernels. In: Proc. of the 19th Int. Conf. on Machine Learning, pp. 179-186 (2002)
8. Kummamuru, K., Krishnapuram, R., Agrawal, R.: On learning asymmetric dissimilarity measures. In: International Conference on Data Mining, p. 4. IEEE (2005)
9. Maron, O., Lozano-Perez, T.: A framework for multiple-instance learning. In: Advances in Neural Information Processing Systems, pp. 570-576. Morgan Kaufmann Publishers (1998)
10. Paredes, R., Vidal, E.: A class-dependent weighted dissimilarity measure for nearest neighbor classification problems. Pattern Recognition Letters 21(12), 1027-1036 (2000)
11. Pekalska, E., Duin, R.P.W.: The dissimilarity representation for pattern recognition: foundations and applications, vol. 64. World Scientific Pub. Co. Inc. (2005)
12. Pekalska, E., Duin, R., Pacl?k, P.: Prototype selection for dissimilarity-based classifiers. Pattern Recognition 39(2), 189-208 (2006)
13. Rahmani, R., Goldman, S., Zhang, H., Krettek, J., Fritts, J.: Localized content based image retrieval. In: Proc. of the 7th ACM SIGMM International Workshop on Multimedia Information Retrieval, pp. 227-236. ACM (2005)
14. Sørensen, L., Loog, M., Tax, D., Lee, W., de Bruijne, M., Duin, R.: Dissimilarity-Based Multiple Instance Learning. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) SSPR&SPR 2010. LNCS, vol. 6218, pp. 129-138. Springer, Heidelberg (2010)
15. Srinivasan, A., Muggleton, S., King, R.: Comparing the use of background knowledge by inductive logic programming systems. In: Proceedings of the 5th International Workshop on Inductive Logic Programming, pp. 199-230 (1995)
16. Tao, Q., Scott, S., Vinodchandran, N., Osugi, T.: Svm-based generalized multipleinstance learning via approximate box counting. In: Proc. of the 21st Int. Conf. on Machine learning, p. 101. ACM (2004)
17. Tax, D., Loog, M., Duin, R., Cheplygina, V., Lee, W.: Bag dissimilarities for multiple instance learning. Similarity-Based Pattern Recognition, 222-234 (2011)
18. Wang, J.: Solving the multiple-instance problem: A lazy learning approach. In: Proc. of the 17th Int. Conf. on Machine Learning (2000)
19. Weinberger, K., Saul, L.: Distance metric learning for large margin nearest neighbor classification. The Journal of Machine Learning Research 10, 207-244 (2009)
20. Zafra, A., Pechenizkiy, M., Ventura, S.: Reducing dimensionality in multiple instance learning with a filter method. Hybrid Artificial Intelligence Systems, 35-44 (2010)
21. Zhao, C., Shi, W., Deng, Y.: A new hausdorff distance for image matching. Pattern Recognition Letters 26(5), 581-586 (2005)