Domain adaptation in regression

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6925 LNAI, Issue , 2011, Pages 308-323

  Cortes, Corinna ^a   Mohri, Mehryar ^a,b  

^a GOOGLE INC   (United States)

^b NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTATION ALGORITHMS; DOMAIN ADAPTATION; EFFICIENT ALGORITHM; FEATURE SPACE; GAUSSIAN KERNELS; GENERAL CLASS; HIGH-DIMENSIONAL FEATURE SPACE; LARGE DATASETS; LOSS FUNCTIONS; NEW RESULTS; REGULARIZATION ALGORITHMS; REPRODUCING KERNEL HILBERT SPACES;

ALGORITHMS;

EID: 80054098743     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24412-4_25     Document Type: Conference Paper

References (21)

1. Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: NIPS 2006 (2007)
2. Ben-David, S., Lu, T., Luu, T., Pál, D.: Impossibility theorems for domain adaptation. Journal of Machine Learning Research - Proceedings Track 9, 129-136 (2010)
3. Blitzer, J., Dredze, M., Pereira, F.: Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification. In: ACL 2007 (2007)
4. Blitzer, J., Crammer, K., Kulesza, A., Pereira, F.,Wortman, J.: Learning bounds for domain adaptation. In: NIPS 2007 (2008)
5. Cormen, T., Leiserson, C., Rivest, R.: Introduction to Algorithms. The MIT Press, Cambridge (1992)
6. Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning 20(3) (1995)
7. Dredze, M., Blitzer, J., Talukdar, P.P., Ganchev, K., Graca, J., Pereira, F.: Frustratingly Hard Domain Adaptation for Parsing. In: CoNLL 2007 (2007)
8. Dudley, R.M.: Real Analysis and Probability. Wadsworth, Belmont (1989)
9. Jiang, J., Zhai, C.: Instance Weighting for Domain Adaptation in NLP. In: Proceedings of ACL 2007, pp. 264-271 (2007)
10. Legetter, C.J., Woodland, P.C.: Maximum likelihood linear regression for speaker adaptation of continuous density hidden Markov models. Comp. Speech and Lang. (1995)
11. Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation: Learning bounds and algorithms. In: Proceedings of COLT 2009. Omnipress, Montréal, Canada (2009)
12. Martínez, A.M.: Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Trans. Pattern Anal. 24(6) (2002)
13. 2). Soviet Mathematics Doklady 27(2), 372-376 (1983)
14. Nesterov, Y.: Smooth minimization of non-smooth functions. Math. Program. 103, 127-152 (2005)
15. Nesterov, Y.: Smoothing technique and its applications in semidefinite optimization. Math. Program. 110, 245-259 (2007)
16. Nesterov, Y., Nemirovsky, A.: Interior Point Polynomial Methods in Convex Programming: Theory and Appl. SIAM, Philadelphia (1994)
17. Pietra, S.D., Pietra, V.D., Mercer, R.L., Roukos, S.: Adaptive language modeling using minimum discriminant estimation. In: HLT 1991: Workshop on Speech and Nat. Lang. (1992)
18. Rosenfeld, R.: A Maximum Entropy Approach to Adaptive Statistical Language Modeling. Computer Speech and Language 10, 187-228 (1996)
19. Saunders, C., Gammerman, A., Vovk, V.: Ridge Regression Learning Algorithm in Dual Variables. In: ICML (1998)
20. Steinwart, I.: On the influence of the kernel on the consistency of support vector machines. JMLR 2, 67-93 (2002)
21. Vapnik, V.N.: Statistical Learning Theory. J. Wiley & Sons, Chichester (1998)