Bayesian active distance metric learning

Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, UAI 2007

Volumn , Issue , 2007, Pages 442-449

  Yang, L ^a   Jin, Rong ^a   Sukthankar, Rahul ^b  

^a Michigan State University   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN APPROACHES; BAYESIAN FRAMEWORKS; CLASSIFICATION ACCURACY; CONTENT BASED IMAGE RETRIEVAL; DISTANCE METRIC LEARNING; DISTANCE METRICS; EFFICIENT ALGORITHM; NON-BAYESIAN APPROACH; PAIRWISE CONSTRAINTS; POINT ESTIMATION; POSTERIOR DISTRIBUTIONS; RELATIVE DISTANCES; STATISTICAL CLASSIFICATION; TRAINING EXAMPLE; VARIATIONAL METHODS;

BAYESIAN NETWORKS; LEARNING ALGORITHMS;

ARTIFICIAL INTELLIGENCE;

EID: 80053218518     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (32)

1. Abe, N., & Mamitsuka, H. (1998). Query learning strategies using boosting and bagging. Proc. Int. Conf. on Mach. Learn.
2. Bar-Hillel, A., Hertz, T., Shental, N., & Weinshall, D. (2003). Learning distance functions using equivalence relations. Proc. Int. Conf. on Mach. Learn.
3. Campbell, C, Cristianini, N., & Smola, A. J. (2000). Query learning with large margin classifiers. Proc. Int. Conf. on Mach. Learn.
4. Cohn, D. A., Ghahramani, Z., & Jordan, M. I. (1995). Active learning with statistical models. NIPS.
5. Domeniconi, C, & Gunopulos, D. (2002). Adaptive nearest neighbor classification using support vector machines. NIPS.
6. Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Mach. Learn., 28.
7. Friedman, J. (1994). Flexible metric nearest neighbor classification (Technical Report). Stanford University Statistics Department.
8. Globerson, A., & Roweis, S. (2006). Metric learning by collapsing classes. NIPS.
9. Goldberger, J., Roweis, S., Hinton, G., & Salakhutdinov, R. (2005). Neighbourhood components analysis. NIPS.
10. Hastie, T., & Tibshirani, R. (1996). Discriminant adaptive nearest neighbor classification. IEEE Trans. Pattern Anal. Mach. Intell., 18.
11. Hofmann, T., & Buhmann, J. M. (1997). Active data clustering. NIPS.
12. Hoi, S. C. H., Liu, W., Lyu, M. R., & Ma, W.-Y. (2006). Learning distance metrics with contextual constraints for image retrieval. Proc. Computer Vision and Pattern Recognition.
13. Jaakkola, T. S., & Jordan, M. I. (2000). Bayesian parameter estimation via variational methods. Statistics and Computing, 10.
14. Jin, R., & Si, L. (2004). A bayesian approach toward active learning for collaborative filtering. Proc. Uncertainty in Artificial Intelligence.
15. Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999). An introduction to variational methods for graphical models. Mach. Learn., 37, 183-233. (Pubitemid 30544678)
16. Kwok, J. T., & Tsang, I. W. (2003). Learning with idealized kernels. Proc. Int. Conf. on Mach. Learn.
17. MacKay, D. (1992). Information-based objective functions for active data selection. Neural Computation, 4.
18. Melville, P., & Mooney, R. J. (2004). Diverse ensembles for active learning. Proc. Int. Conf. on Mach. Learn.
19. Newman, D. J., Hettich, S., Blake, C. L., & Merz, C. J. (1998). UCI repository of mach. learn. databases. http://www.ics.uci.edu/~mlearn/ MLRepository.html.
20. Roy, N., & McCallum, A. (2001). Toward optimal active learning through sampling estimation of error reduction. Proc. Int. Conf. on Mach. Learn.
21. Schultz, M., & Joachims, T. (2004). Learning a distance metric from relative comparisons. NIPS.
22. Seung, H. S., Opper, M., & Sompolinsky, H. (1992). Query by committee. Computational Learning Theory.
23. Sugato Basu, A. B., & Mooney, R. J. (2004). Active semisupervision for pairwise constrained clustering. Proc. the SIAMInt. Conf. on Data Mining.
24. Tong, S., & Koller, D. (2000a). Active learning for parameter estimation in bayesian networks. NIPS.
25. Tong, S., & Koller, D. (2000b). Support vector machine active learning with applications to text classification. Proc. Int. Conf. on Mach. Learn.
26. Weinberger, K., Blitzer, J., & Saul, L. (2006). Distance metric learning for large margin nearest neighbor classification. NIPS.
27. X. Zhu, J. L., & Ghahramani, Z. (2003). Combining active learning and semi-supervised learning using gaussian fields and harmonic functions. Proc. ICML workshop on the Continuum from Labeled to Unlabeled Data.
28. Xing, E., Ng, A., Jordan, M., & Russell, S. (2003). Distance metric learning with application to clustering with side-information. NIPS.
29. Yang, L., & Jin, R. (2006). Distance metric learning: A comprehensive survey. http://www.cse.msu.edu/~yangliu1/frame-survey-v2.pdf.
30. Yang, L., Jin, R., Sukthankar, R., & Liu, Y. (2006). An efficient algorithm for local distance metric learning. Proc. AAAI.
31. Zhang, T., & Oles, F. J. (2000). A probability analysis on the value of unlabeled data for classification problems. Proc. Int. Conf. on Mach. Learn.
32. Zhang, Y, Xu, W., & Callan, J. R (2003). Exploration and exploitation in adaptive filtering based on bayesian active learning. Proc. Int. Conf. on Mach. Learn.