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2004

Exponentiated Gradient Algorithms for Large-margin Structured Classification

9 years 2 months ago
Exponentiated Gradient Algorithms for Large-margin Structured Classification
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.
Peter L. Bartlett, Michael Collins, Benjamin Taska
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where NIPS
Authors Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester
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