A theory of learning from different domains

Author:

Ben-David Shai,Blitzer John,Crammer Koby,Kulesza Alex,Pereira Fernando,Vaughan Jennifer Wortman

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference33 articles.

1. Ando, R., & Zhang, T. (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6, 1817–1853.

2. Anthony, M., & Bartlett, P. (1999). Neural network learning: theoretical foundations. Cambridge: Cambridge University Press.

3. Bartlett, P., & Mendelson, S. (2002). Rademacher and Gaussian complexities: risk bounds and structural results. Journal of Machine Learning Research, 3, 463–482.

4. Batu, T., Fortnow, L., Rubinfeld, R., Smith, W., & White, P. (2000). Testing that distributions are close. In: IEEE symposium on foundations of computer science (Vol. 41, pp. 259–269).

5. Baxter, J. (2000). A model of inductive bias learning. Journal of Artificial Intelligence Research, 12, 149–198.

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