Domain adaptation-can quantity compensate for quality?

Annals of Mathematics and Artificial Intelligence

Volumn 70, Issue 3, 2014, Pages 185-202

  Ben David, Shai ^a   Urner, Ruth ^a  

^a UNIVERSITY OF WATERLOO   (Canada)

Author keywords

Domain adaptation; Machine learning; Sample complexity

Indexed keywords

EID: 84899445570     PISSN: 10122443     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10472-013-9371-9     Document Type: Article

References (14)

1. Ben-David, S., and Urner, R.: On the hardness of domain adaptation and the utility of unlabeled target samples. In: ALT, pp. 139-153 (2012).
2. Ben-David, S., Blitzer, J., Crammer, K., Pereira, F.: Analysis of representations for domain adaptation. In: NIPS, pp. 137-144 (2006).
3. Cortes, C., Mansour, Y., Mohri, M.: Learning bounds for importance weighting. In: Lafferty, J., Williams, C. K. I., Shawe-Taylor, J., Zemel, R., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 23, pp. 442-450 (2010).
4. Daumé III, H., Jagarlamudi, J.: Domain adaptation for machine translation by mining unseen words. In: Association for Computational Linguistics (2011).
5. Gong, B., Shi, Y., Sha, F., Grauman, K: Geodesic flow kernel for unsupervised domain adaptation. In: CVPR, pp. 2066-2073 (2012).
6. Haussler, D., Welzl, E.: Epsilon-nets and simplex range queries. In: Proceedings of the Second Annual Symposium on Computational Geometry, SCG '86, pp. 61-71. New York, NY, USA, ACM (1986).
7. Huang, J., Gretton, A., Schölkopf, B., Smola, A. J., Borgwardt, K. M.: Correcting sample selection bias by unlabeled data. In: NIPS. MIT Press, Cambridge (2007).
8. Kifer, D., Ben-David, S., Gehrke, J.: Detecting change in data streams. In: VLDB, pp. 180-191 (2004).
9. Mansour, Y., Mohri, M., Rostamizadeh, A.: Domain adaptation: Learning bounds and algorithms. In: COLT (2009).
10. Shalev-Shwartz, S., Ben-David, S.: Understanding machine learning. Cambridge University Press (2014, in press).
11. Steinwart, I., Scovel, C.: Fast rates for support vector machines. Ann. Statist. 35(2), 575-607 (2007).
12. Sugiyama, M., Mueller, K.: Generalization error estimation under covariate shift. In: Workshop on Information-Based Induction Sciences (2005).
13. Urner, R., Ben-David, S., Shalev-Shwartz, S.: Supplementay material to: Unlabeled data can speed-up prediction time. http://www. cs. uwaterloo. ca/~rurner/SSLSupplementICML2011. pdf (2011).
14. Urner, R., Ben-David, S., Shalev-Shwartz, S.: Unlabeled data can speed up prediction time. In: ICML (2011).