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Convex learning with invariances

Teo, Choon-Hui; Globerson, Amir; Roweis, Sam; Smola, Alexander


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

CollectionsANU Research Publications
Date published: 2008
Type: Conference paper
Source: Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference


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