Exponentiated Gradient Algorithms for Large-margin Structured Classification

Part of Advances in Neural Information Processing Systems 17 (NIPS 2004)

Bibtex Metadata Paper

Authors

Peter Bartlett, Michael Collins, Ben Taskar, David McAllester

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 struc- ture. 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 expecta- tions under Gibbs distributions can be calculated efficiently. The method for structured labels relies on a more general result, specifically the ap- plication of exponentiated gradient updates [7, 8] to quadratic programs.