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Convex learning with invariances

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Date

2008

Authors

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

Journal Title

Journal ISSN

Volume Title

Publisher

MIT Press

Abstract

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

Description

Keywords

Keywords: Column generation; Common problems; Loss functions; Optimization algorithms; Optimization problems; Learning algorithms

Citation

URI

http://hdl.handle.net/1885/53583

Collections

ANU Research Publications

Source

Advances in Neural Information Processing Systems 20: Proceedings of the 2007 Conference

Type

Conference paper

Book Title

Entity type

Access Statement

License Rights

DOI

Restricted until

2037-12-31

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Description
01_Teo_Convex_learning_with_2008.pdf (374.38 KB)

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