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.

• Type: Conference
• Year: 2004
• Where: NIPS
• Authors: Peter L. Bartlett, Michael Collins, Benjamin Taskar, David A. McAllester
• Keywords: Gibbs Distributions, Markov Random Fields, NIPS 2004, NIPS 2007, Structured