Algorithms for active learning

Cost-Sensitive Machine Learning

Volumn , Issue , 2011, Pages 3-49

  Settles, Burr ^a  

^a Carnegie Mellon University ^*  (United States)

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EID: 85059391225     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (68)

1. N. Abe and H. Mamitsuka. Query learning strategies using boosting and bagging. In Proceedings of the International Conference on Machine Learning (ICML), pages 1-9. Morgan Kaufmann, 1998.
2. D. Angluin. Queries and concept learning. Machine Learning, 2:319-342, 1988.
3. M.F. Balcan, S. Hanneke, and J. Wortman. The true sample complexity of active learning. In Proceedings of the Conference on Learning Theory (COLT), pages 45-56. Springer, 2008.
4. E. Baum. Neural net algorithms that learn in polynomial time from examples and queries. IEEE Transactions on Neural Networks, 2(1), 1991.
5. A.L. Berger, V.J. Della Pietra, and S.A. Della Pietra. A maximum entropy approach to natural language processing. Computational Linguis- tics, 22(1):39-71, 1996.
6. K. Brinker. Incorporating diversity in active learning with support vector machines. In Proceedings of the International Conference on Machine Learning (ICML), pages 59-66. AAAI Press, 2003.
7. R. Burbidge, J.J. Rowland, and R.D. King. Active learning for regression based on query by committee. In Proceedings of Intelligent Data Engineering and Automated Learning (IDEAL), pages 209-218. Springer, 2007.
8. K. Chaloner and I. Verdinelli. Bayesian experimental design: A review. Statistical Science, 10(3):237-304, 1995.
9. D. Cohn. Neural network exploration using optimal experiment design. In Advances in Neural Information Processing Systems (NIPS), volume 6, pages 679-686. Morgan Kaufmann, 1994.
10. D. Cohn, L. Atlas, and R. Ladner. Improving generalization with active learning. Machine Learning, 15(2):201-221, 1994.
11. D. Cohn, L. Atlas, R. Ladner, M. El-Sharkawi, R. Marks II, M. Aggoune, and D. Park. Training connectionist networks with queries and selective sampling. In Advances in Neural Information Processing Systems (NIPS). Morgan Kaufmann, 1990.
12. D. Cohn, Z. Ghahramani, and M.I. Jordan. Active learning with statistical models. Journal of Artificial Intelligence Research, 4:129-145, 1996.
13. T.M. Cover and J.A. Thomas. Elements of Information Theory. Wiley, 2006.
14. N. Cressie. Statistics for Spatial Data. Wiley, 1991.
15. A. Culotta and A. McCallum. Reducing labeling effort for stuctured prediction tasks. In Proceedings of the National Conference on Artificial Intelligence (AAAI), pages 746-751. AAAI Press, 2005.
16. I. Dagan and S. Engelson. Committee-based sampling for training probabilistic classifiers. In Proceedings of the International Conference on Machine Learning (ICML), pages 150-157. Morgan Kaufmann, 1995.
17. S. Danziger, J. Zeng, Y. Wang, R. Brachmann, and R. Lathrop. Choosing where to look next in a mutation sequence space: Active learning of informative p53 cancer rescue mutants. Bioinformatics, 23(13):1104-1114, 2007.
18. V. Federov. Theory of Optimal Experiments. Academic Press, 1972.
19. P. Flaherty, M. Jordan, and A. Arkin. Robust design of biological experiments. In Advances in Neural Information Processing Systems (NIPS), volume 18, pages 363-370. MIT Press, 2006.
20. Y. Freund, H.S. Seung, E. Shamir, and N. Tishby. Selective samping using the query by committee algorithm. Machine Learning, 28:133-168, 1997.
21. A. Fujii, T. Tokunaga, K. Inui, and H. Tanaka. Selective sampling for example-based word sense disambiguation. Computational Linguistics, 24(4):573-597, 1998.
22. S. Geman, E. Bienenstock, and R. Doursat. Neural networks and the bias/variance dilemma. Neural Computation, 4:1-58, 1992.
23. C. Guestrin, A. Krause, and A.P. Singh. Near-optimal sensor placements in Gaussian processes. In Proceedings of the International Conference on Machine Learning (ICML), pages 265-272. ACM Press, 2005.
24. Y. Guo and R. Greiner. Optimistic active learning using mutual information. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pages 823-829. AAAI Press, 2007.
25. Y. Guo and D. Schuurmans. Discriminative batch mode active learning. In Advances in Neural Information Processing Systems (NIPS), number 20, pages 593-600. MIT Press, 2008.
26. A. Hauptmann, W. Lin, R. Yan, J. Yang, and M.Y. Chen. Extreme video retrieval: joint maximization of human and computer performance. In Proceedings of the ACM Workshop on Multimedia Image Retrieval, pages 385-394. ACM Press, 2006.
27. S.C.H. Hoi, R. Jin, and M.R. Lyu. Large-scale text categorization by batch mode active learning. In Proceedings of the International Confer- ence on the World Wide Web, pages 633-642. ACM Press, 2006.
28. R. Hwa. Sample selection for statistical parsing. Computational Linguis- tics, 30(3):73-77, 2004.
29. A. Kapoor, E. Horvitz, and S. Basu. Selective supervision: Guiding supervised learning with decision-theoretic active learning. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI), pages 877-882. AAAI Press, 2007.
30. R.D. King, J. Rowland, S.G. Oliver, M. Young, W. Aubrey, E. Byrne, M. Liakata, M. Markham, P. Pir, L.N. Soldatova, A. Sparkes, K.E. Whelan, and A. Clare. The automation of science. Science, 324(5923):85-89, 2009.
31. R.D. King, K.E. Whelan, F.M. Jones, P.G. Reiser, C.H. Bryant, S.H. Muggleton, D.B. Kell, and S.G. Oliver. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature, 427(6971):247-52, 2004.
32. C. Körner and S. Wrobel. Multi-class ensemble-based active learning. In Proceedings of the European Conference on Machine Learning (ECML), pages 687-694. Springer, 2006.
33. V. Krishnamurthy. Algorithms for optimal scheduling and management of hidden Markov model sensors. IEEE Transactions on Signal Processing, 50(6):1382-1397, 2002.
34. K. Lang and E. Baum. Query learning can work poorly when a human oracle is used. In Proceedings of the IEEE International Joint Conference on Neural Networks, pages 335-340. IEEE Press, 1992.
35. D. Lewis and W. Gale. A sequential algorithm for training text classifiers. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, pages 3-12. ACM/Springer, 1994.
36. M. Lindenbaum, S. Markovitch, and D. Rusakov. Selective sampling for nearest neighbor classifiers. Machine Learning, 54(2):125-152, 2004.
37. Y. Liu. Active learning with support vector machine applied to gene expression data for cancer classiffcation. Journal of Chemical Information and Computer Sciences, 44:1936-1941, 2004.
38. D. MacKay. Information-based objective functions for active data selection. Neural Computation, 4(4):590-604, 1992.
39. A. McCallum and K. Nigam. Employing EM in pool-based active learning for text classiffcation. In Proceedings of the International Conference on Machine Learning (ICML), pages 359-367. Morgan Kaufmann, 1998.
40. P. Melville and R. Mooney. Diverse ensembles for active learning. In Pro- ceedings of the International Conference on Machine Learning (ICML), pages 584-591. Morgan Kaufmann, 2004.
41. P. Melville, S.M. Yang, M. Saar-Tsechansky, and R. Mooney. Active learning for probability estimation using Jensen-Shannon divergence. In Proceedings of the European Conference on Machine Learning (ECML), pages 268-279. Springer, 2005.
42. T. Mitchell. Generalization as search. Artificial Intelligence, 18:203-226, 1982.
43. 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 Proceedings of the German Conference on AI, pages 489-493. Springer, 2007.
44. I. Muslea, S. Minton, and C.A. Knoblock. Selective sampling with redundant views. In Proceedings of the National Conference on Artificial Intelligence (AAAI), pages 621-626. AAAI Press, 2000.
45. H.T. Nguyen and A. Smeulders. Active learning using pre-clustering. In Proceedings of the International Conference on Machine Learning (ICML), pages 79-86. ACM Press, 2004.
46. R. Nowak. The geometry of generalized binary search. Department of electrical and computer engineering, University of Wisconsin-Madison, 2009.
47. N. Roy and A. McCallum. Toward optimal active learning through sampling estimation of error reduction. In Proceedings of the International Conference on Machine Learning (ICML), pages 441-448. Morgan Kaufmann, 2001.
48. T. Scheffer, C. Decomain, and S. Wrobel. Active hidden Markov models for information extraction. In Proceedings of the International Confer- ence on Advances in Intelligent Data Analysis (CAIDA), pages 309-318. Springer-Verlag, 2001.
49. A.I. Schein and L.H. Ungar. Active learning for logistic regression: An evaluation. Machine Learning, 68(3):235-265, 2007.
50. M.J. Schervish. Theory of Statistics. Springer, 1995.
51. G. Schohn and D. Cohn. Less is more: Active learning with support vector machines. In Proceedings of the International Conference on Machine Learning (ICML), pages 839-846. Morgan Kaufmann, 2000.
52. B. Settles. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2009.
53. 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), pages 1069-1078. ACL Press, 2008.
54. B. Settles, M. Craven, and L. Friedland. Active learning with real annotation costs. In Proceedings of the NIPS Workshop on Cost-Sensitive Learning, pages 1-10, 2008.
55. H.S. Seung, M. Opper, and H. Sompolinsky. Query by committee. In Proceedings of the ACM Workshop on Computational Learning Theory, pages 287-294, 1992.
56. C.E. Shannon. A mathematical theory of communication. Bell System Technical Journal, 27:379-423, 623-656, 1948.
57. C. Sutton and A. McCallum. An introduction to conditional random fields for relational learning. In L. Getoor and B. Taskar, editors, Intro- duction to Statistical Relational Learning. MIT Press, 2006.
58. C.A. Thompson, M.E. Califf, and R.J. Mooney. Active learning for natural language parsing and information extraction. In Proceedings of the International Conference on Machine Learning (ICML), pages 406-414. Morgan Kaufmann, 1999.
59. S. Tong and E. Chang. Support vector machine active learning for image retrieval. In Proceedings of the ACM International Conference on Multimedia, pages 107-118. ACM Press, 2001.
60. S. Tong and D. Koller. Support vector machine active learning with applications to text classiffcation. In Proceedings of the International Confer- ence on Machine Learning (ICML), pages 999-1006. Morgan Kaufmann, 2000.
61. G. Tür, D. Hakkani-Tür, and R.E. Schapire. Combining active and semisupervised learning for spoken language understanding. Speech Commu- nication, 45(2):171-186, 2005.
62. Z. Xu, R. Akella, and Y. Zhang. Incorporating diversity and density in active learning for relevance feedback. In Proceedings of the European Con- ference on IR Research (ECIR), pages 246-257. Springer-Verlag, 2007.
63. R. Yan, J. Yang, and A. Hauptmann. Automatically labeling video data using multi-class active learning. In Proceedings of the International Con- ference on Computer Vision, pages 516-523. IEEE Press, 2003.
64. H. Yu. SVM selective sampling for ranking with application to data retrieval. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD), pages 354-363. ACM Press, 2005.
65. C. Zhang and T. Chen. An active learning framework for content based information retrieval. IEEE Transactions on Multimedia, 4(2):260-268, 2002.
66. T. Zhang and F.J. Oles. A probability analysis on the value of unlabeled data for classiffcation problems. In Proceedings of the International Con- ference on Machine Learning (ICML), pages 1191-1198. Morgan Kaufmann, 2000.
67. X. Zhu and A. Goldberg. Introduction to Semi-Supervised Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool, 2009.
68. X. Zhu, J. Lafferty, and Z. Ghahramani. Combining active learning and semi-supervised learning using Gaussian fields and harmonic functions. In Proceedings of the ICML Workshop on the Continuum from Labeled to Unlabeled Data, pages 58-65, 2003.