On the value of pairwise constraints in classification and consistency

ACM International Conference Proceeding Series

Volumn 227, Issue , 2007, Pages 1111-1118

  Zhang, Jian ^a   Yan, Rong ^b  

^a PURDUE UNIVERSITY   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC ANALYSIS; CLASSIFICATION (OF INFORMATION); DECISION THEORY; PARAMETER ESTIMATION; PROBLEM SOLVING;

ASYMPTOTIC VARIANCE; CLASSIFICATION DATASETS; DECISION BOUNDARY; PAIRWISE CONSTRAINTS;

CONSTRAINT THEORY;

EID: 34547966080     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273636     Document Type: Conference Paper

References (18)

1. Bartlett, P. L., Jordan, M. I., & McAuliffe, J. D. (2006). Convexity, classification, and risk bounds. Journal of the American Statistical Association, 101(473), 138-156.
2. Basu, S., Banerjee, A., & Mooney, R. J. (2003). Active semi-supervision for pairwise constrained clustering. Proceedings of the 20th Intl. Conf. on Machine Learning. Washington, DC.
3. Basu, S., Bilenko, M., & Mooney, R. J. (2004). A probabilistic framework for semi-supervised clustering. Proc. of SIGKDD (pp. 59-68).
4. Blatt, M., Wiseman, S., & Domany, E. (1997). Data clustering using a model granular magnet. Neural Computation, 9, 1805-1842.
5. Harville, D. (1997). Matrix algebra from a statistician's perspective. Springer.
6. Knight, K., & Fu, W. (2000). Asymptotics for lasso-type estimators. The Annals of Statistics, 28(5), 1356-1378.
7. Kumar, S., & Hebert, M. (2003). Discriminative random fields: A discriminative framework for contextual interaction in classification. IEEE International Conference on Computer Vision (ICCV).
8. Kwok, J. T., & Tsang, I. W. (2003). Learning with idealized kernel. Proceedings of the 20th Intl. Conf. on Machine Learning. Washington, DC.
9. Shental, N., Bar-Hillel, A., Hertz, T., & Weinshall, D. (2003a). Computing gaussian mixture models with em using side information. Workshop on 'The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining', ICML 2003. Washington, DC.
10. Shental, N., Bar-Hillel, A., Hertz, T., & Weinshall, D. (2003b). Enhancing image and video retrieval: Learning via equivalence constraints. Proc. of CVPR. Madison, WI.
11. Shental, N., Zomet, A., Hertz, T., & Weiss, Y. (2003c). Pairwise clustering and graphical models. NIPS 03.
12. van der Vaart, A. (2000). Asymptotic statistics. Cambridge University Press.
13. Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (2001). Constrained k-means clustering with background knowledge. Proc. of the 18th Intl. Conf. on Machine Learning (pp. 577-584). Morgan Kaufmann Publishers Inc.
14. Xing, E. P., Ng, A. Y., Jordan, M. I., & Russel, S. (2002). Distance metric learning with applications to clustering with side information. Advances in Neural Information Processing Systems.
15. Yan, R., Zhang, J., Yang, J., & Hauptmann, A. (2006). A discriminative learning framework with pairwise constraints for video object classification. IEEE Transactions on Pattern Analysis and Machine. Intelligence, 28(4), 578-593.
16. Yu, S. X., & Shi, J. (2001). Grouping with directed relationships. Lecture Notes in Computer Science, 2134, 283-291.
17. Zass, R., & Shashua, A. (2005). A unifying approach to hard and probabilistic clustering. Proc. of ICCV.
18. Zhang, T. (2004). Statistical behavior and consistency of classification methods based on convex risk minimization. The Annals of Statistics, 32, 56-85.