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Exponentiated gradient algorithms for large-margin structured classification

Bartlett, Peter L., Collins, Michael J., Taskar, Ben, & McAllester, David A. (2005) Exponentiated gradient algorithms for large-margin structured classification. In Saul , Lawrence K., Weiss, Yair, & Bottou, Léon (Eds.) Advances in Neural Information Processing Systems 17 : Proceedings of the 2004 Conference, MIT Press, Hyatt Regency, Vancouver, pp. 113-120.

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Abstract

We consider the problem of structured classification, where the task is to predict a label y from an input x, and y has meaningful internal structure. Our framework includes supervised training of Markov random fields and weighted context-free grammars as special cases. We describe an algorithm that solves the large-margin optimization problem defined in [12], using an exponential-family (Gibbs distribution) representation of structured objects. The algorithm is efficient—even in cases where the number of labels y is exponential in size—provided that certain expectations under Gibbs distributions can be calculated efficiently. The method for structured labels relies on a more general result, specifically the application of exponentiated gradient updates [7, 8] to quadratic programs.

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6 citations in Scopus
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ID Code: 44012
Item Type: Conference Paper
Additional URLs:
Keywords: Neural Information Processing Systems, Markov random fields
ISBN: 9780262195348
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > Mathematical Sciences
Copyright Owner: Copyright 2005 MIT Press
Deposited On: 18 Aug 2011 08:13
Last Modified: 18 Aug 2011 08:14

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