Multiple source adaptation and the Rényi divergence

Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009

Volumn , Issue , 2009, Pages 367-374

  Mansour, Yishay ^a   Mohri, Mehryar ^b   Rostamizadeh, Afshin ^c  

^a TEL AVIV UNIVERSITY   (Israel)

^b GOOGLE INC   (United States)

^c NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

SENTIMENT ANALYSIS;

ARTIFICIAL DATA; LABELING FUNCTIONS; LOWER BOUNDS; MULTIPLE SOURCE; PERFORMANCE BENEFITS; SOURCE DISTRIBUTION; THEORETICAL STUDY;

ARTIFICIAL INTELLIGENCE;

EID: 71049149704     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. C. Arndt. Information Measures: Information and its Description in Science and Engineering. Signals and Communication Technology. Springer Verlag, 2004.
2. S. Ben-David, J. Blitzer, K. Crammer, and F. Pereira. Analysis of representations for domain adaptation. In Proceedings of NIPS 2006. MIT Press, 2007.
3. J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman. Learning bounds for domain adaptation. In Proceedings of NIPS 2007. MIT Press, 2008.
4. J. Blitzer, M. Dredze, and F. Pereira. Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In ACL, 2007.
5. K. Crammer, M. Kearns, and J. Wortman. Learning from multiple sources. JMLR, 9:1757-1774, 2008.
6. H. Daumé III and D. Marcu. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26:101-126, 2006. (Pubitemid 44452208)
7. M. Dredze, J. Blitzer, P. P. Talukdar, K. Ganchev, J. Graca, and F. Pereira. Frustratingly Hard Domain Adaptation for Parsing. In CoNLL, 2007.
8. J.-L. Gauvain and Chin-Hui. Maximum a posteriori estimation for multivariate gaussian mixture observations of Markov chains. IEEE Transactions on Speech and Audio Processing, 2(2):291-298, 1994.
9. F. Jelinek. Statistical Methods for Speech Recognition. The MIT Press, 1998.
10. J. Jiang and C. Zhai. Instance Weighting for Domain Adaptation in NLP. In Proceedings of ACL 2007, pages 264-271, Prague, Czech Republic, 2007.
11. C. J. Legetter and P. C.Woodland. Maximum likelihood linear regression for speaker adaptation of continuous density hidden markov models. Computer Speech and Language, pages 171-185, 1995.
12. Y.Mansour, M.Mohri, and A. Rostamizadeh. Domain adaptation with multiple sources. In NIPS 2008, 2009.
13. A. M. Martínez. Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. Mach. Intell., 24(6):748-763, 2002. (Pubitemid 34669589)
14. S. D. Pietra, V. D. Pietra, R. L. Mercer, and S. Roukos. Adaptive language modeling using minimum discriminant estimation. In HLT '91: Proceedings of the workshop on Speech and Natural Language, pages 103-106, 1992.
15. B. Roark and M. Bacchiani. Supervised and unsupervised PCFG adaptation to novel domains. In HLT-NAACL, 2003.
16. R. Rosenfeld. A Maximum Entropy Approach to Adaptive Statistical Language Modeling. Computer Speech and Language, 10:187-228, 1996.
17. L. G. Valiant. A theory of the learnable. ACM Press New York, NY, USA, 1984.
18. V. N. Vapnik. Statistical Learning Theory. Wiley-Interscience, New York, 1998.