Learning Kernels

Description

Kernel-based algorithms have been used with great success in a variety of machine learning applications. But, the choice of the kernel, which is crucial to the success of these algorithms, has been traditionally entirely left to the user. Rather than requesting the user to select a specific kernel, learning kernel algorithms require the user only to specify a family of kernels. This family of kernels can be used by a learning algorithm to form a combined kernel and derive an accurate predictor.


Related Publications
Corinna Cortes, Marius Kloft, and Mehryar Mohri.
Learning kernels using local Rademacher complexity.
In Advances in Neural Information Processing Systems (NIPS 2013). Lake Tahoe, Nevada, 2013. MIT Press.

Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh.
Multi-class classification with maximum margin multiple kernel.
In Proceedings of the Thirtieth International Conference on Machine Learning (ICML 2013). Atlanta, GA, June 2013.

Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh.
Algorithms for learning kernels based on centered alignment.
Journal of Machine Learning Research (JMLR), 13:795-828, 2012.

Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh.
Tutorial: Learning Kernels.
ICML 2011, Bellevue, Washington, July 2011.

Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh.
Generalization bounds for learning kernels.
In Proceedings of the Twenty-seventh International Conference on Machine Learning (ICML 2010). Haifa, Israel, June 2010.

Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh.
Two-stage learning kernel methods.
In Proceedings of the Twenty-seventh International Conference on Machine Learning (ICML 2010). Haifa, Israel, June 2010.

Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh.
Learning non-linear combinations of kernels.
In Advances in Neural Information Processing Systems (NIPS 2009). Vancouver, Canada, 2009. MIT Press.

Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh.
Learning sequence kernels.
In Proceedings of IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2008), (invited lecture). Cancún, Mexico, October 2008.