Active learning from relative queries

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2013, Pages 1614-1620

  Qian, Buyue ^a   Wang, Xiang ^a   Wang, Fei ^b   Li, Hongfei ^b   Ye, Jieping ^c   Davidson, Ian ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

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

^c ARIZONA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE LEARNING; ACTIVE LEARNING METHODS; BETTER PERFORMANCE; NEIGHBORHOOD INFORMATION; REAL DATA SETS; REAL PROBLEMS; RELATIVE INFORMATION; SIDE-INFORMATION;

ARTIFICIAL INTELLIGENCE;

EID: 84896061426     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Rita Chattopadhyay, Zheng Wang, Wei Fan, Ian Davidson, Sethuraman Panchanathan, and Jieping Ye. Batch mode active sampling based on marginal probability distribution matching. In KDD, pages 741-749, 2012.
2. Aron Culotta and Andrew McCallum. Reducing labeling effort for structured prediction tasks. In AAAI, pages 746-751, 2005.
3. Aristides Gionis, Piotr Indyk, and Rajeev Motwani. Similarity search in high dimensions via hashing. In VLDB, pages 518-529, 1999.
4. Aristides Gionis, Theodoros Lappas, and Evimaria Terzi. Estimating entity importance via counting set covers. In KDD, pages 687-695, 2012.
5. Yuhong Guo and Russ Greiner. Optimistic active learning using mutual information. In IJCAI, pages 823-829, 2007.
6. Steven C. H. Hoi, Rong Jin, Jianke Zhu, and Michael R. Lyu. Batch mode active learning and its application to medical image classification. In ICML, pages 417-424, 2006.
7. Ashish Kapoor, Eric Horvitz, and Sumit Basu. Selective supervision: Guiding supervised learning with decision-theoretic active learning. In IJCAI, pages 877-882, 2007.
8. David D. Lewis and William A. Gale. A sequential algorithm for training text classifiers. In SIGIR, pages 3-12, 1994.
9. C. Lintott, K. Schawinski, S. Bamford, A. Slosar, K. Land, D. Thomas, E. Edmondson, K. Masters, R. C. Nichol, M. J. Raddick, A. Szalay, D. Andreescu, P. Murray, and J. Vandenberg. Galaxy Zoo 1: data release of morphological classifications for nearly 900 000 galaxies. Monthly Notices of the Royal Astronomical Society, 410:166-178, January 2011.
10. Prem Melville and Raymond J. Mooney. Diverse ensembles for active learning. In ICML, pages 74-81, 2004.
11. I. Muslea, S. Minton, and C. Knoblock. Selective sampling with redundant views. In Proceedings of the National Conference on Artificial Intelligence, 2000.
12. Rina Panigrahy. An improved algorithm finding nearest neighbor using kd-trees. In LATIN, pages 387-398, 2008.
13. Parisa Rashidi and Diane J. Cook. Ask me better questions: active learning queries based on rule induction. In KDD, pages 904-912, 2011.
14. Sam T. Roweis and Lawrence K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, 290:2323-2326, 2000.
15. Nicholas Roy and Andrew Mccallum. Toward optimal active learning through sampling estimation of error reduction. In ICML, pages 441-448, 2001.
16. Burr Settles and Mark Craven. An analysis of active learning strategies for sequence labeling tasks. In EMNLP, pages 1070-1079, 2008.
17. Burr Settles, Mark Craven, and Soumya Ray. Multiple-instance active learning. In NIPS, pages 1289-1296, 2008.
18. B. Settles. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2009.
19. Omer Tamuz, Ce Liu, Serge Belongie, Ohad Shamir, and Adam Kalai. Adaptively learning the crowd kernel. In ICML, pages 673-680, 2011.
20. Vijay V. Vazirani. Approximation algorithms. Springer-Verlag New York, Inc., New York, NY, USA, 2001.
21. Fei Wang and Changshui Zhang. Label propagation through linear neighborhoods. In ICML, pages 985-992, 2006.
22. Fabian L. Wauthier, Nebojsa Jojic, and Michael I. Jordan. Active spectral clustering via iterative uncertainty reduction. In KDD, pages 1339-1347, 2012.
23. Tong Zhang and Frank J. Oles. A probability analysis on the value of unlabeled data for classification problems. In ICML, pages 1191-1198, 2000.
24. Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schölkopf. Learning with local and global consistency. In NIPS, 2003.
25. Xiaojin Zhu, Zoubin Ghahramani, and John D. Lafferty. Semi-supervised learning using gaussian fields and harmonic functions. In ICML, pages 912-919, 2003.
26. Xiaojin Zhu, John Lafferty, and Zoubin Ghahramani. Combining active learning and semi supervised learning using gaussian fields and harmonic functions. In ICML 2003 Workshop on the Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, pages 58-65, 2003.