Humans perform semi-supervised classification too

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2007, Pages 864-869

  Zhu, Xiaojin ^a   Rogers, Timothy ^b   Qian, Ruichen ^a   Kalish, Chuck ^b  

^a University of Wisconsin Madison   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FEEDBACK; LEARNING SYSTEMS; MATHEMATICAL MODELS; MODAL ANALYSIS;

BIMODAL DISTRIBUTION; GAUSSIAN MIXTURE MODEL;

CLASSIFICATION (OF INFORMATION);

EID: 36348995973     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Baluja, S. 1998. Probabilistic modeling for face orientation discrimination: Learning from labeled and unlabeled data. NIPS.
2. Castelli, V., and Cover, T. 1996. The relative value of labeled and unlabeled samples in pattern recognition with an unknown mixing parameter. IEEE Trans. Information Theory 42(6):2101-2117.
3. Chapelle, O.; Zien, A.; and Schölkopf, B., eds. 2006. Semisupervised learning. MIT Press.
4. Corduneanu, A., and Jaakkola, T. 2001. Stable mixing of complete and incomplete information. Technical Report AIM-2001-030, MIT.
5. Cozman, F.; Cohen, I.; and Cirelo, M. 2003. Semi-supervised learning of mixture models. In ICML-03.
6. Dempster, A.; Laird, N.; and Rubin, D. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B.
7. Gelman, A.; Carlin, J. B.; Stern, H. S.; and Rubin, D. B. 2004. Bayesian data analysis. Chapman & Hall/CRC, second edition. Gielis, J. 2003. A generic geometric transformation that unifies a wide range of natural and abstract shapes. American Journal of Botany 90(3):333-338.
8. Graf Estes, K.; Evans, J. L.; Alibali, M. W.; and Saffran, J. R. 2006. Can infants map meaning to newly segmented words'? Statistical segmentation and word learning. Psychological Science. Langley, P. 2006. Intelligent behavior in humans and machines. Technical report, Stanford University.
9. Love, B. 2002. Comparing supervised and unsupervised category learning. Psychonomic Bulletin & Review 9(4):829-835.
10. Mitchell, T. 2006. The discipline of machine learning. Technical Report CMU-ML-06-108, Carnegie Mellon University.
11. Mozer, M.; Jones, M.; and Shettel, M. 2006. Context effects in category learning: An investigation of four probabilistic models. In NIPS.
12. Nigam, K.; McCallum, A. K.; Thrun, S.; and Mitchell, T. 2000. Text classification from labeled and unlabeled documents using EM. Machine Learning 39(2/3): 103-134.
13. Ratsaby, J., and Venkatesh, S. 1995. Learning from a mixture of labeled and unlabeled examples with parametric side information. COLT.
14. Stromsten, S. B. 2002. Classification learning from both classified and unclassified examples. Ph.D. Dissertation, Stanford.
15. Tenenbaum, J. B., and Xu, F. 2000. Word learning as Bayesian inference. In Proc. Cognitive Science Society.
16. Tenenbaum, J. B. 1999. A Bayesian framework for concept learning. Ph.D. Dissertation, MIT.
17. Zhu, X. 2005. Semi-supervised learning literature survey. Technical Report 1530, Univ. Wisconsin-Madison.