Share  Bookmark

 Download from


Ando, R. K., & Zhang, T. (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6, 18171853.
Argyriou, A., Evgeniou, T., & Pontil, M. (2008). Convex multitask feature learning. Machine Learning, 73(3), 243272.
Argyriou, A., Pontil, M., Ying, Y., & Micchelli, C. A. (2007). A spectral regularization framework for multitask structure learning. In Nips.
Audiffren, J., & Kadri, H. (2013). Stability of multitask kernel regression algorithms. In Proceedings of acml (pp. 116).
Bartlett, P., Kulkarni, S., & Posner, S. (1997). Covering numbers for realvalued function classes. IEEE Transactions on Information Theory, 43(5), 17211724.
Bartlett, P. L., & Mendelson, S. (2003). Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research, 3, 463482.
Baxter, J. (2000). A model of inductive bias learning. Journal of Artificial Intelligence Research, 12(1), 149198.
Koltchinskii, V. (2001). Rademacher penalties and structural risk minimization. IEEE Transactions on Information Theory, 47(5), 19021914.