Convex learning with invariances
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
|Collections||ANU Research Publications|
|Source:||Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference|
|01_Teo_Convex_learning_with_2008.pdf||374.38 kB||Adobe PDF||Request a copy|
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