MILIS: Multiple instance learning with instance selection

IEEE Transactions on Pattern Analysis and Machine Intelligence

Volumn 33, Issue 5, 2011, Pages 958-977

  Fu, Zhouyu ^a   Robles Kelly, Antonio ^b,c   Zhou, Jun ^b,c  

^a UNIVERSITY OF BALLARAT   (Australia)

^b CSIRO   (Australia)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

alternating optimization; feature selection; Multiple instance learning; support vector machine

Indexed keywords

ALTERNATING OPTIMIZATION; CLASSIFIER LEARNING; FEATURE SELECTION; INSTANCE SELECTION; KERNEL DENSITY ESTIMATORS; MULTIPLE INSTANCE LEARNING; OPTIMIZATION FRAMEWORK; REAL WORLD DATA; SPEED-UPS; STATE OF THE ART; TRAINING DATA SETS; TRAINING PROCESS;

ADAPTIVE ALGORITHMS; OPTIMIZATION; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

EID: 79953031810     PISSN: 01628828     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPAMI.2010.155     Document Type: Article

References (37)

1. T.G. Dietterich, R.H. Lathrop, and T. Lozano-Perez, "Solving the Multiple-Instance Problem with Axis-Parallel Rectangles," Artifi cial Intelligence, vol. 89, pp. 31-71, 1997. (Pubitemid 127412230)
2. S. Ray and M. Craven, "Supervised versus Multiple Instance Learning: An Empirical Comparison," Proc. Int'l Conf. Machine Learning, pp. 697-704, 2005. (Pubitemid 43183395)
3. Y. Chen and J.Z. Wang, "Image Categorization by Learning and Reasoning with Regions," J. Machine Learning Research, vol. 5, pp. 913-939, 2004.
4. Y. Chen, J. Bi, and J. Wang, "MILES: Multiple-Instance Learning via Embedded Instance Selection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 1931-1947, Dec. 2006. (Pubitemid 46405705)
5. O. Maron and T. Lozano-Perez, "A Framework for Multiple-Instance Learning," Proc. Conf. Advances in Neural Information Processing Systems, pp. 570-576, 1998.
6. Q. Zhang and S. Goldman, "Em-DD: An Improved Multiple-Instance Learning Technique," Proc. Conf. Advances in Neural Information Processing Systems, pp. 1073-1080, 2002.
7. R. Rahmani, S.A. Goldman, H. Zhang, S.R. Cholleti, and J.E. Fritts, "Localized Content Based Image Retrieval," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1902-2002, Nov. 2008.
8. J. Wang and J.D. Zucker, "Solving the Multiple-Instance Problem: A Lazy Learning Approach," Proc. Int'l Conf. Machine Learning, pp. 1119-1125, 2000.
9. B.E. Boser, I. Guyon, and V. Vapnik, "A Training Algorithm for Optimal Margin Classifiers," Proc. ACM Conf. Computational Learning Theory, pp. 144-152, 1992. (Pubitemid 23615470)
10. T. Gartner, A. Flach, A. Kowalczyk, and A.J. Smola, "Multi-Instance Kernels," Proc. Int'l Conf. Machine Learning, pp. 179-186, 2002.
11. S. Andrews, I. Tsochantaridis, and T. Hofmann, "Support Vector Machines for Multiple-Instance Learning," Proc. Conf. Advances in Neural Information Processing Systems, pp. 561-568, 2003.
12. P.-M. Cheung and J.T. Kwok, "A Regularization Framework for Multiple-Instance Learning," Proc. Int'l Conf. Machine Learning, pp. 193-200, 2006.
13. Z.-H. Zhou and J.-M. Xu, "On the Relation between Multi-Instance Learning and Semi-Supervised Learning," Proc. Int'l Conf. Machine Learning, pp. 1167-1174, 2007.
14. O.L. Mangasarian and E.W. Wild, "Multiple Instance Classification via Successive Linear Programming," J. Optimization Theory and Applications, vol. 137, no. 3, pp. 555-568, 2008.
15. P. Viola, J.C. Platt, and C. Zhang, "Multiple Instance Boosting for Object Detection," Proc. Conf. Neural Information Processing Systems, 2005.
16. S.R. Cholleti, S.A. Goldman, and R. Rahmani, "Mi-Winnow: A New Multiple-Instance Learning Algorithm," Proc. Int'l Conf. Tools with Artificial Intelligence, pp. 336-346, 2006.
17. 1;2 Logistic Regression," Proc. Int'l Conf. Pattern Recognition, 2008.
18. J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, "1-Norm Support Vector Machines," Proc. Conf. Neural Information Processing Systems, 2003.
19. R.O. Duda and P.E. Hart, Pattern Classification. Wiley, 2000.
20. S. Arya, D.M. Mount, N.S. Netanyahu, R. Silverman, and A.Y. Wu, "An Optimal Algorithm for Approximate Nearest Neighbor Searching Fixed Dimensions," J. ACM, vol. 45, no. 6, pp. 891-923, 1998.
21. J. Platt, "Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Methods," Advances in Large Margin Classifiers, pp. 61-74, MIT Press, 2000.
22. O. Chapelle, "Training a Support Vector Machine in the Primal," Neural Computation, vol. 19, pp. 1155-1178, 2007.
23. T. Joachims, "Training Linear SVMs in Linear Time," Proc. ACM Conf. Knowledge Discovery and Data Mining, 2006.
24. R.E. Fan, K.W. Chang, C.J. Hsieh, X.R. Wang, and C.J. Lin, "Liblinear: A Library for Large Linear Classification," J. Machine Learning Research, vol. 9, pp. 1871-1874, 2008.
25. A. Dempster, N. Laird, and D. Rubin, "Maximum-Likelihood from Incomplete Data via the EM Algorithm," J. Royal Statistical Soc. Series B (Methodological), vol. 39, pp. 1-38, 1977.
26. T. Minka, "Expectation-Maximization as Lower Bound Maximization," 1998.
27. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene Selection for Cancer Classification Using Support Vector Machines," Machine Learning, vol. 46, nos. 1-3, pp. 389-422, 2002. (Pubitemid 34129977)
28. T. Hastie and R. Tibshirani, "Classification by Pairwise Coupling," Proc. Conf. Advances in Neural Information Processing Systems, vol. 10, 1998.
29. R. Rifkin and A. Klautau, "In Defense of One-vs-All Classification," J. Machine Learning Research, vol. 5, pp. 101-141, 2004.
30. K. Crammer and Y. Singer, "On the Algorithmic Implementation of Multiclass Kernel-Based Vector Machines," J. Machine Learning Research, vol. 2, pp. 265-292, 2001.
31. L. Bottou and O. Bousquet, "The Tradeoffs of Large Scale Learning," Proc. Conf. Advances in Neural Information Processing Systems, vol. 20, pp. 161-168, 2008.
32. L. Fei-Fei, R. Fergus, and P. Perona, "Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories," Proc. IEEE CVPR Workshop Generative-Model Based Vision, 2004.
33. K. Grauman and T. Darrell, "The Pyramid Match Kernel: Efficient Learning with Sets of Features," J. Machine Learning Research, vol. 8, pp. 725-760, 2007. (Pubitemid 46677049)
34. S. Lazebnik, C. Schmid, and J. Ponce, "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
35. J. Zhang, M. Marszalek, S. Lazebnik, and C. Schmid, "Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study," Int'l J. Computer Vision, vol. 73, no. 2, pp. 213-238, 2007. (Pubitemid 46181625)
36. A. Bosch, A. Zisserman, and X. Munoz, "Image Classification Using ROIs and Multiple Kernel Learning," Int'l J. Computer Vision, 2008.
37. M. Varma and D. Ray, "Learning the Discriminative Power-Invariance Trade-Off," Proc. Int'l Conf. Computer Vision, 2007.