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
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.