Training conditional random fields for maximum labelwise accuracy

Samuel S. Gross, Olga Russakovsky, Chuong B. Do, Serafim Batzoglou

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

We consider the problem of training a conditional random field (CRF) to maximize per-label predictive accuracy on a training set, an approach motivated by the principle of empirical risk minimization. We give a gradient-based procedure for minimizing an arbitrarily accurate approximation of the empirical risk under a Hamming loss function. In experiments with both simulated and real data, our optimization procedure gives significantly better testing performance than several current approaches for CRF training, especially in situations of high label noise.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference
Pages529-536
Number of pages8
StatePublished - Dec 1 2007
Externally publishedYes
Event20th Annual Conference on Neural Information Processing Systems, NIPS 2006 - Vancouver, BC, Canada
Duration: Dec 4 2006Dec 7 2006

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other20th Annual Conference on Neural Information Processing Systems, NIPS 2006
CountryCanada
CityVancouver, BC
Period12/4/0612/7/06

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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    Gross, S. S., Russakovsky, O., Do, C. B., & Batzoglou, S. (2007). Training conditional random fields for maximum labelwise accuracy. In Advances in Neural Information Processing Systems 19 - Proceedings of the 2006 Conference (pp. 529-536). (Advances in Neural Information Processing Systems).