Learning sparse kernel classifiers for multi-instance classification

IEEE Transactions on Neural Networks and Learning Systems

Volumn 24, Issue 9, 2013, Pages 1377-1389

  Fu, Zhouyu ^a   Lu, Guojun ^b   Ting, Kai Ming ^b   Zhang, Dengsheng ^b  

^a UNIVERSITY OF WESTERN SYDNEY   (Australia)

^b UNIVERSITY OF BALLARAT   (Australia)

Author keywords

Classification; convex optimization; kernel machines; multi instance learning (MI)

Indexed keywords

KERNEL MACHINE; MULTI-INSTANCE LEARNING; NONCONVEX OPTIMIZATION PROBLEM; OBJECTIVE FUNCTIONS; OPTIMIZATION PROBLEMS; OPTIMIZATION STRATEGY; PREDICTIVE PERFORMANCE; SPARSE KERNEL CLASSIFIERS;

BENCHMARKING; CLASSIFICATION (OF INFORMATION); CONVEX OPTIMIZATION; OPTIMIZATION;

PROBLEM SOLVING;

EID: 84882876311     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2013.2254721     Document Type: Article

References (25)

1. T. G. Dietterich, R. H. Lathrop, and T. Lozano-Perez, "Solving the multiple-instance problem with axis-parallel rectangles," Artif. Intell., vol. 89, nos. 1-2, pp. 31-71, Jan. 1997. (Pubitemid 127412230)
2. T. Gartner, A. Flach, A. Kowalczyk, and A. J. Smola, "Multi-instance kernels," in Proc. Int. Conf. Mach. Learn., 2002, pp. 179-186.
3. S. Andrews, I. Tsochantaridis, and T. Hofmann, "Support vector machines for multiple-instance learning," in Proc. Neural Inf. Process. Syst., 2003, pp. 561-568.
4. P.-M. Cheung and J. T.-Y. Kwok, "A regularization framework for multiple-instance learning," in Proc. 23rd Int. Conf. Mach. Learn., Jun. 2006, pp. 193-200.
5. Z.-H. Zhou and J.-M. Xu, "On the relation between multi-instance learning and semi-supervised learning," in Proc. 24th Int. Conf. Mach. Learn., 2007, pp. 1167-1174.
6. F. Li and C. Sminchisescu, "Convex multiple-instance learning by estimating likelihood ratio," in Proc. Adv. Neural Inf. Process. Syst., Dec. 2010, pp. 1-15.
7. Y.-F. Li, J. T. Kwok, I. Tsang, and Z.-H. Zhou, "A convex method for locating regions of interest with multi-instance learning," in Proc. Eur. Conf. Mach. Learn., Oct. 2009, pp. 1-16.
8. Y. Chen, J. Bi, and J. Wang, "Miles: Multiple-instance learning via embedded instance selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 1931-1947, Dec. 2006. (Pubitemid 46405705)
9. S. Keerthi, O. Chapelle, and D. DeCoste, "Building support vector machines with reduced classifier complexity," J. Mach. Learn. Res., vol. 7, pp. 1493-1515, Jul. 2006. (Pubitemid 44024591)
10. M. Wu, B. Scholkopf, and G. Bakir, "A direct method for building sparse kernel learning algorithms," J. Mach. Learn. Res., vol. 7, pp. 603-624, Apr. 2006. (Pubitemid 43668113)
11. Z. Fu, A. Robles-Kelly, and J. Zhou, "MILIS: Multiple instance learning with instance selection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 5, pp. 958-977, May 2011.
12. C. Williams and M. Seeger, "Using the Nyström method to speed up kernel machines," in Proc. Adv. Neural Inf. Process. Syst., 2001, pp. 682-688.
13. Y.-J. Lee and O. L. Mangasarian, "RSVM: Reduced support vector machines," in Proc. 1st SIAM Int. Conf. Data Mining, 2001, pp. 5-7.
14. A. J. Smola and B. Scholkopf, "Sparse greedy matrix approximation for machine learning," in Proc. Int. Conf. Mach. Learn., Jul. 2000, pp. 911-918.
15. B. Scholkopf and A. J. Smola, Learning With Kernels: SVMs, Regularization, Optimization and Beyond. Cambridge, MA, USA: MIT Press, 2002.
16. R. Collobert, S. Bengio, and Y. Bengio, "A parallel mixture of SVMs for very large scale problems," in Proc. Neural Comput., May 2002, pp. 1105-1114.
17. C.-Y. G. F. Chang, X.-R. Lin, and C.-J. Lu, "Tree decomposition for large-scale SVM problems," J. Mach. Learn. Res., vol. 11, pp. 2935-2972, Oct. 2010.
18. Z. Fu, A. Robles-Kelly, and J. Zhou, "Mixing linear SVMs for nonlinear classification," IEEE Trans. Neural Netw., vol. 21, no. 12, pp. 1963-1975, Dec. 2010.
19. Z. Fu, G. Lu, K. Ting, and D. Zhang, "Learning sparse support vector machines for multi-instance classification," in Proc. Eur. Conf. Mach. Learn. Knowl. Discovery Databases, 2011, pp. 471-486.
20. J. Bezdek and R. Harthaway, "Convergence of alternating optimization," Neural Parallel Sci. Comput., vol. 11, no. 4, pp. 351-368, Dec. 2003. (Pubitemid 37506735)
21. A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet, "SimpleMKL," J. Mach. Learn. Res., vol. 9, pp. 2491-2521, Nov. 2008.
22. J. F. Bonnans and A. Shapiro, "Optimization problems with pertubation: A guided tour," SIAM Rev., vol. 40, no. 2, pp. 202-227, 1998.
23. G. Tzanetakis and P. Cook, "Music genre classification of audio signals," IEEE Trans. Speech Audio Proc., vol. 10, no. 5, pp. 293-302, Jul. 2002. (Pubitemid 34950067)
24. D. G. Lowe, "Distinctive image features from scale-invariant keypoints," Int. J. Comput. Vis., vol. 60, no. 2, pp. 91-110, Jul. 2004.
25. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories," in Proc. Comput. Vis. Pattern Recognit., vol. 2. 2006, pp. 2169-2178. (Pubitemid 44931582)