A Fast Implementation of Semi-Markov Conditional Random Fields

  • La The Vinh
  • Sungyoung Lee
  • Young-Koo Lee
Conference paper

DOI: 10.1007/978-3-642-27183-0_9

Part of the Communications in Computer and Information Science book series (CCIS, volume 260)
Cite this paper as:
The Vinh L., Lee S., Lee YK. (2011) A Fast Implementation of Semi-Markov Conditional Random Fields. In: Kim T., Adeli H., Ramos C., Kang BH. (eds) Signal Processing, Image Processing and Pattern Recognition. Communications in Computer and Information Science, vol 260. Springer, Berlin, Heidelberg

Abstract

Recently, Conditional Random Fields (CRF) model has been used and proved to be a good model for sequential modeling. It, however, lacks the capability of duration modeling. Therefore, some researchers introduced semi Markov Conditional Random Fields (semi-CRF) to take into account the duration distribution and showed some improvements. Nevertheless, the training algorithms for semi-CRF require quite a high complexity making semi-CRF impractical in some large-scale problems. Therefore, in this work we propose a fast implementation of the training algorithm in order to reduce the complexity required by semi-CRF. Our theoretical analysis as well as experiments’ result show a noticeable improvement in computation time, which is about ten times less than that of the original algorithm.

Keywords

Conditional Random Fields Semi-Markov Model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • La The Vinh
    • 1
  • Sungyoung Lee
    • 1
  • Young-Koo Lee
    • 1
  1. 1.Dept. of Computer EngineeringKyung Hee UniversityKorea

Personalised recommendations