Information Technology Reference
In-Depth Information
6. I. Dagan and S. P. Engelson, “Committee-based sampling for training probabilis-
tic classifiers,” in Proceedings of the Twelfth International Conference on Machine
Learning (Tahoe City, CA, USA), pp. 150-157, Morgan Kaufmann, 1995.
7. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby, “Information, prediction, and
query by committee,” in Advances in Neural Information Processing Systems 5, [NIPS
Conference] , pp. 483-490, Morgan Kaufmann, 1993.
8. S. Tong and D. Koller, “Support vector machine active learning with applications to
text classification,” Journal of Machine Learning Research , vol. 2, pp. 45-66, 2002.
9. N. Roy and A. McCallum, “Toward optimal active learning through sampling esti-
mation of error reduction,” in ICML , Morgan Kaufmann, 2001.
10. R. Moskovitch, N. Nissim, D. Stopel, C. Feher, R. Englert, and Y. Elovici, “Improving
the detection of unknown computer worms activity using active learning,” in Proceed-
ings of the 30th Annual German Conference on Advances in Artificial Intelligence ,KI
'07, pp. 489-493, Springer-Verlag, 2007.
11. Y. Guo and R. Greiner, “Optimistic active learning using mutual information,” in Pro-
ceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI'07
(Hyderabad, India), pp. 823-829, 2007.
12. B. Settles and M. Craven, “An analysis of active learning strategies for sequence
labeling tasks,” in Proceedings of the Conference on Empirical Methods in Natural
Language Processing , EMNLP '08 (Jeju Island, Korea), pp. 1070-1079, Association
for Computational Linguistics, 2008.
13. J. Zhu, H. Wang, T. Yao, and B. K. Tsou, “Active learning with sampling by uncer-
tainty and density for word sense disambiguation and text classification,” in COLING
'08 , Association for Computational Linguistics, 2008.
14. H. T. Nguyen and A. Smeulders, “Active learning using pre-clustering,” in ICML ,
ACM, 2004.
15. A. K. Mccallum and K. Nigam, “Employing EM in pool-based active learning for
text classification,” in ICML , 1998.
16. P. Donmez, J. G. Carbonell, and P. N. Bennett, “Dual strategy active learning,” in
ECML '07 , Springer, 2007.
17. P. Donmez and J. Carbonell, “Paired sampling in density-sensitive active learning,”
in Proceedings of 10th International Symposium on Artificial Intelligence and Math-
ematics (Ft. Lauderdale, FL, USA), 2008.
18. K. Tomanek and U. Hahn, “Reducing class imbalance during active learning for
named entity annotation,” in K-CAP '09 , 105-112, ACM, 2009.
19. S. Ertekin, J. Huang, L. Bottou, and C. L. Giles, “Learning on the border: Active
learning in imbalanced data classification,” in Proceedings of the 16th ACM Con-
ference on Information and Knowledge Management (CIKM) (Lisbon, Portugal), pp.
127-136, ACM, 2007.
20. M. Bloodgood and K. V. Shanker, “Taking into account the differences between
actively and passively acquired data: The case of active learning with support vector
machines for imbalanced datasets,” in NAACL '09 , Association for Computational
Linguistics, 2009.
21. S. Ertekin, Learning in Extreme Conditions: Online and Active Learning with Massive,
Imbalanced and Noisy Data . PhD thesis, The Pennsylvania State University, 2009.
Search WWH ::




Custom Search