Convex learning with invariances
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Teo, Choon-Hui
Globerson, Amir
Roweis, Sam
Smola, Alexander
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MIT Press
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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
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Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference
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2037-12-31
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