Convex learning with invariances
Teo, Choon-Hui; Globerson, Amir; Roweis, Sam; Smola, Alexander
Description
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 |
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Date published: | 2008 |
Type: | Conference paper |
URI: | http://hdl.handle.net/1885/53583 |
Source: | Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference |
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01_Teo_Convex_learning_with_2008.pdf | 374.38 kB | Adobe PDF | Request a copy |
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