Modeling multiple annotator expertise in the semi-supervised learning scenario

Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

Volumn , Issue , 2010, Pages 674-682

  Yan, Yan ^a   Rosales, Rómer ^b   Fung, Glenn ^b   Dy, Jennifer ^a  

^a Northeastern University   (United States)

^b SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; LEARNING ALGORITHMS; LEARNING SYSTEMS;

DATA POINTS; LABELED AND UNLABELED DATA; MODEL AND ALGORITHMS; NUMERICAL COMPARISON; ON-LINE COLLABORATIONS; SEMI-SUPERVISED; UNLABELED DATA;

SEMI-SUPERVISED LEARNING;

EID: 80053140206     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. M. Belkin, I.Matveeva, and P. Niyogi. Regularization and semi-supervised learning on large graphs. In Proceedings of theWorkshop on Computational Learning Theory, Morgan Kaufmann, 2004.
2. K. P. Bennett and A. Demiriz. Semi-supervised support vectormachines. In Advances in Neural Information Processing Systems, pages 368-374. MIT Press, 1998.
3. P. Berkhin. Survey of clustering data mining techniques. In J. Kogan, C. Nicholas, and M. Teboulle, editors, Grouping Multidimensional Data, chapter 2. Springer-Verlag, 2006.
4. A. Blum and S. Chawla. Learning from labeled and unlabeled data using graph mincuts. In International Conference on Machine Learning, pages 19-26.Morgan Kaufmann, 2001.
5. A. Corduneanu and T. Jaakkola. Distributed information regularization on graphs. In Advances in Neural Information Processing Systems, 2004.
6. T. Cover and J. Thomas. Elements of Information Theory. Wiley Interscience, 1991.
7. A. P. Dawid and A. M. Skeene. Maximum likeihood estimation of observed error-rates using the EM algorithm. Applied Statistics, 28:20-28, 1979.
8. A. Frank and A. Asuncion. UCI machine learning repository. http://archive.ics.uci.edu/ml.University of California, Irvine, School of Information and Computer Sciences, 2010.
9. G. Fung and O. L. Mangasarian. Semi-supervised support vector machines for unlabeled data classification. Optimization Methods and Software, 15:29-44, 2001. (Pubitemid 33817502)
10. J. Howe. Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business. Crown Business, 2008.
11. R. Jin and Z. Ghahramani. Learning with multiple labels. In Advances in Neural Information Processing Systems, 2003.
12. T. Joachims. Transductive inference for text classification using support vector machines. In International Conference on Machine Learning, pages 200-209. Morgan Kaufmann, 1999.
13. B. Krishnapuram, D. Williams, Y. Xue, A. Hartemink, L. Carin, and M. Figueiredo. On semi-supervised classification. In Advances in Neural Information Processing Systems, pages 721-728. MIT Press, 2005.
14. M. Qazi, G. Fung, S. Krishnan, R. Rosales, H. Steck, B. Rao, D. D. Poldermans, and D. Chandrasekaran. Automated heartwall motion abnormality detection from ultrasound images using Bayesian networks. In International Joint Conference on Artificial Intelligence, 2007.
15. V. C. Raykar, S. Yu, L. Zhao, A. Jerebko, C. Florin, G. Hermosillo-Valadez, L. Bogoni, and L. Moy. Supervised learning from multiple experts: Whom to trust when everyone lies a bit. In International Conference on Machine Learning, 2009.
16. R. Snow, B. O'Connor,D. Jurafsky, andA. Ng. Cheap and fast - but is it good? Evaluating non-expert annotations for natural language tasks. In Conference on Empirical Methods on Natural Language Processing, 2008.
17. A. Sorokin and D. Forsyth. Utility data annotation with Amazon Mechanical Turk. In Workshop on Internet Vision. Computer Vision and Pattern Recognition, 2008.
18. D. J. Spiegelhalter and P. Stovin. An analysis of repeated biopsies following cardiac transplantation. Statistics in Medicine, 2(1):33-40, Jan-Mar 1983. (Pubitemid 13084314)
19. Y. Yan, R. Rosales, G. Fung, M. Schmidt, G. Hermosillo, L. Bogoni, L. Moy, and J. Dy. Modeling annotator expertise: Learning when everybody knows a bit of something. In International Conference on Artificial Intelligence and Statistics, 2010.
20. D. Zhou, O. Bousquet, T. N. Lal, J. Weston, and B. Schölkopf. Learning with local and global consistency. In Advances in Neural Information Processing Systems. MIT Press, 2003.
21. X. Zhu. Semi-supervised learning literature survey. Technical Report TR-1530, Computer Sciences Department, University of Wisconsin Madison, 2006.