Incorporating invariances into a learning algorithm is a common problem in machine learning. We provide a convex formulation which can deal with arbitrary loss functions and arbitrary losses. In addition, it is a drop-in replacement for most optimization algorithms for kernels, including solvers of the SVMStruct family. The advantage of our setting is that it relies on column generation instead of modifying the underlying optimization problem directly.
|Title of host publication||Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference|
|State||Published - 2009|
|Event||21st Annual Conference on Neural Information Processing Systems, NIPS 2007 - Vancouver, BC, Canada|
Duration: 3 Dec 2007 → 6 Dec 2007
|Name||Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference|
|Conference||21st Annual Conference on Neural Information Processing Systems, NIPS 2007|
|Period||3/12/07 → 6/12/07|