Batch mode active learning and its application to medical image classification

ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning

Volumn 2006, Issue , 2006, Pages 417-424

  Hoi, Steven C H ^a   Jin, Hong ^b   Zhu, Jianke ^a   Lyu, Michael R ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

^b Michigan State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTATION THEORY; ITERATIVE METHODS; MATHEMATICAL MODELS; MEDICAL IMAGING;

BATCH MODE ACTIVE LEARNING; GREEDY ALGORITHM; MANUAL LABELING;

LEARNING SYSTEMS;

EID: 33749263388     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121-167.
2. Campbell, C., Cristianini, N., & Smola, A. J. (2000). Query learning with large margin classifiers. Int. Conf. on Machine Learning (ICML) (pp. 111-118).
3. Fine, S., Gilad-Bachrach, R., & Shamir, E. (2002). Query by committee, linear separation and random walks. Theor. Comput. Sci., 284, 25-51.
4. Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Mach. Learn., 28, 133-168.
5. Graepel, T., & Herbrich, R. (2000). The kernel gibbs sampler. NIPS 13 (pp. 514-520).
6. Lehmann, T. M., Güld, G. O., T.Deselaers, Keysers, D., Schubert, H., Spitzer, K., Ney, H., & Wein, B. B. (2005). Automatic categorization of medical images for content-based retrieval and data mining. Computerized Medical Imaging and Graphics, 29, 143-155.
7. Liu, C., & Wechsler, H. (2002). Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Processing, 11, 467-476.
8. Luo, T., Kramer, K., Samson, S., & Remsen, A. (2004). Active learning to recognize multiple types of plankton (pp. III: 478-481.).
9. McCallum, A. K., & Nigam, K. (1998). Employing EM and pool-based active learning for text classification. Int. Conf. on Machine Learning (pp. 350-358).
10. Müller, H., Michoux, N., Bandon, D., & Geissbuhler, A. (2004). A review of content-based image retrieval systems in medical applications - clinical benefits and future directions. Int J Med Inform, 73, 1-23.
11. Nemhauser, G., Wolsey, L., & Fisher, M. (1978). An analysis of the approximations for maximizing sub-modular set functions. Mathematical Programming, 14, 265-294.
12. Parker, R. G. (1988). Discrete optimization. Academic Press.
13. Roy, N., & McCallum, A. (2001). Toward optimal active learning through sampling estimation of error reduction. 18th ICML (pp. 441-448).
14. Schohn, G., & Cohn, D. (2000). Less is more: Active learning with support vector machines. 17th ICML (pp. 839-846).
15. Seeger, M. (2001). Learning with labeled and unlabeled data (Technical Report). University of Edinburgh.
16. Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. Computational Learning Theory (pp. 287-294).
17. Shen, X., & Zhai, C. (2005). Active feedback in ad hoc information retrieval. Proc. ACM SIGIR'05 (pp. 59-66). Salvador, Brazil.
18. Tong, S., & Koller, D. (2000). Support vector machine active learning with applications to text classification. 17th ICML (pp. 999-1006). Stanford, US.
19. Zhang, T., & Oles, F. J. (2000). A probability analysis on the value of unlabeled data for classification problems. 17th ICML (pp. 1191-1198). Stanford, US.
20. Zhu, J., & Hastie, T. (2001). Kernel logistic regression and the import vector machine. Advances in Neural Information Processing Systems 14 (pp. 1081-1088).