Name
Abstract | ||
|---|---|---|
We present a simplified EM algorithm and an approximate algorithm for training hierarchical hidden Markov models (HHMMs), an extension of hidden Markov models. The EM algorithm we present is proved to increase the likelihood of training sentences at each iteration unlike the existing algorithm called the generalized Baum-Welch algorithm. The approximate algorithm is applicable to tasks like robot navigation in which we observe sentences and train parameters simultaneously. These algorithms and their derivations are simplified by making use of stochastic context-free grammars. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1002/ecjc.10172 | Electronics and Communications in Japan Part Iii-fundamental Electronic Science |
Keywords | DocType | Volume |
stochastic context-free grammar,existing algorithm,robot navigation,train parameter,hidden markov model,simplified training algorithms,training sentence,generalized baum-welch algorithm,hierarchical hidden markov models,hierarchical hidden markov model,em algorithm,approximate algorithm,stochastic context free grammar | Conference | 87 |
Issue | ISSN | ISBN |
5 | 1042-0967 | 3-540-42956-5 |
Citations | PageRank | References |
1 | 0.37 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nobuhisa Ueda | 1 | 369 | 20.78 |
| T. Sato | 2 | 1506 | 137.10 |