Adaptive kernel diverse density estimate for multiple instance learning

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

Volumn 6871 LNAI, Issue , 2011, Pages 185-198

  Xu, Tao ^a   Gondra, Iker ^b   Chiu, David ^a  

^a UNIVERSITY OF GUELPH   (Canada)

^b ST FRANCIS XAVIER UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE KERNELS; DATA SETS; DENSITY ESTIMATES; KERNEL DENSITY ESTIMATE; MULTIPLE INSTANCE LEARNING; OBJECTIVE FUNCTIONS;

DATA MINING; LEARNING SYSTEMS; PATTERN RECOGNITION;

ESTIMATION;

EID: 80052312701     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23199-5_14     Document Type: Conference Paper

References (21)

1. Andrews, S., Tsochantaridis, I., Hofmann, T.: Support vector machines for multiple-instance learning. In: Advances in Neural Information Processing Systems, vol. 15, pp. 561-568 (2003)
2. Cao, R., Cuevas, A., Manteiga, W.G.: A comparative study of several smoothing methods in density estimation. Computational Statistics and Data Analysis 17(2), 153-176 (1994)
3. Chevaleyre, Y., Zucker, J.D.: Solving multiple-instance and multiple-part learning problems with decision trees and rule sets. Application to the mutagenesis problem. In: Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence, pp. 204-214 (2001)
4. Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603-619 (2002)
5. Dietterich, T.G., Lathrop, R.H., Pérez, T.L.: Solving the multiple instance problem with axis-parallel rectangles. Artificial Intelligence 89(1-2), 31-71 (1997)
6. Epanechnikov, V.: Non-parametric estimation of a multivariate probability density. Theory of Probability and its Applications, 153-158 (1969)
7. Gondra, I., Xu, T.: Adaptive mean shift-based image segmentation using multiple instance learning. In: Proceedings of the Third IEEE International Conference on Digital Information Management, pp. 716-721 (2008)
8. Gondra, I., Xu, T.: Image region re-weighting via multiple instance learning. Signal, Image and Video Processing 4(4), 409-417 (2010)
9. Jones, M., Marron, J., Sheather, S.J.: A brief survey of bandwidth selection for density estimation. Journal of the American Statistical Association 91, 401-407 (1996)
10. Maron, O., Lakshmi Ratan, A.: Multiple-instance learning for natural scene classification. In: Proceedings of the 15th International Conference on Machine Learning, vol. 15, pp. 341-349 (1998)
11. Maron, O., Perez, T.L.: A framework for multiple-instance learning. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 570-576 (1998)
12. Park, B., Marron, J.: Comparison of data-driven bandwidth selectors. Journal of the American Statistical Society 85, 66-72 (1990)
13. Park, B., Turlach, B.: Practical performance of several data driven bandwidth selectors (with discussion). Computational Statistics 7, 251-270 (1992)
14. Parzen, E.: On estimation of a probability density function and mode. Annals of Mathematical Statistics 33, 1065-1076 (1962)
15. Rosenblatt, M.: Remarks on some nonparametric estimates of a density function. Annals of Mathematical Statistics 27, 832-837 (1956)
16. Tao, Q., Scott, S., Vinodchandran, N.V., Osugi, T.T.: SVM-based generalized multiple-instance learning via approximate box counting. In: Proceedings of the 21st International Conference on Machine Learning, pp. 779-806 (2004)
17. Terrell, D.G., Scott, D.W.: Variable kernel density estimation. Annals of Statistics 20, 1236-1265 (1992)
18. Wang, J., Zucker, J.D.: Solving the multiple-instance problem: A lazy learning approach. In: Proceedings of the 17th International Conference on Machine Learning, pp. 1119-1126 (2000)
19. Yang, C., Lozano-Pérez, T.: Image database retrieval with multiple-instance learning techniques. In: Proceedings of IEEE International Conference on Data Engineering, pp. 233-243 (2000)
20. Zhang, M.L., Zhou, Z.H.: Adapting RBF neural networks to multi-instance learning. Neural Process. Lett. 23(1), 1-26 (2006)
21. Zhang, Q., Goldman, S.A.: EM-DD: An improved multiple-instance learning technique. In: Advances in Neural Information Processing Systems, vol. 14, pp. 1073-1080. MIT Press, Cambridge (2001)