Active learning via transductive experimental design

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 1081-1088

  Kai, Yu ^a   Jinbo, Bi ^b   Tresp, Volker ^a  

^a SIEMENS AG   (Germany)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL METHODS; DATA REDUCTION; MATHEMATICAL MODELS; OPTIMIZATION; PROBLEM SOLVING; REGRESSION ANALYSIS;

ACTIVE LEARNING; INFORMATIVE EXPERIMENTS; SEQUENTIAL GREEDY; SPATIAL SENSOR;

LEARNING SYSTEMS;

EID: 34250738945     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143980     Document Type: Conference Paper

References (18)

1. Atkinson, A. C., & Donev, A. N. (1992). Optimum experiment designs. Oxford Statistical Science Series. Oxford University Press.
2. Bezdek, J., & Hathaway, R. (2003). Convergence of alternating optimization. Neural. Parallel Sci. Comput., 11, 351-368.
3. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
4. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (pp. 43-52).
5. Chapelle, O. (2005). Active learning for Parzen window classifier. Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics (pp. 49-56).
6. Cohn, D., & Ghahramani, Z. (1996). Active learning with statistical models. Journal of Artificial Intelligence Research, 4, 129-145.
7. Flaherty, P., Jordan, M. I., & Arkin, A. P. (2006). Robust design of biological experiments. NIPS 18.
8. Freund, Y., Seung, H. S., Shamir, E., & Tishby, N. (1997). Selective sampling using the query by committee algorithm. Machine Learning, 28, 133-168.
9. Guestrin, C., Krause, A., & Singh, A. (2005). Near-optimal sensor placements in gaussian processes. Proc. of the International Conference on Machine Learning (ICML).
10. MacKay, D. (1992). Information-based objective functions for active data selection. Neural Computation, 4, 590-604.
11. Natarajan, B. (1995). Sparse approximate solutions to linear systems. SIAM Journal of Computing, 25, 227-234.
12. Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. M. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 99, 103-134.
13. Seeger, M. (2000). Learning with labeled and unlabeled data (Technical Report). Edinburgh University.
14. Tong, S. (2001). Active learning: Theory and applicaitons. Doctoral dissertation, Stanford University.
15. Weston, J., Elisseeff, A., Schölkopf, B., & Tipping, M. (2003). Use of the zero-norm with linear models and kernel methods. Journal of Machine Learning Research, 3, 1439-1461.
16. Zhang, J., & Yang, Y. (2003). Robustness of regularized linear classifcation methods in text categorization. The 26th Annual International SIGIR Conference (SIGIR'99).
17. Zhou, D., Bousquet, O., Lai, T., Weston, J., & Schölkopf, B. (2004). Learning with local and global consistency. Advances in Neural Information Processing Systems 16. MIT Press.
18. Zhu, X. (2005). Semi-supervised learning literature survey.