Machine Learning

, Volume 75, Issue 3, pp 297–325

Search-based structured prediction

Article

DOI: 10.1007/s10994-009-5106-x

Cite this article as:
Daumé, H., Langford, J. & Marcu, D. Mach Learn (2009) 75: 297. doi:10.1007/s10994-009-5106-x

Abstract

We present Searn, an algorithm for integrating search and learning to solve complex structured prediction problems such as those that occur in natural language, speech, computational biology, and vision. Searn is a meta-algorithm that transforms these complex problems into simple classification problems to which any binary classifier may be applied. Unlike current algorithms for structured learning that require decomposition of both the loss function and the feature functions over the predicted structure, Searn is able to learn prediction functions for any loss function and any class of features. Moreover, Searn comes with a strong, natural theoretical guarantee: good performance on the derived classification problems implies good performance on the structured prediction problem.

Keywords

Structured prediction Search Reductions 
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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.School of ComputingUniversity of UtahSalt Lake CityUSA
  2. 2.Yahoo! Research LabsNew YorkUSA
  3. 3.Information Sciences InstituteMarina del ReyUSA