Foundations of statistical natural language processing
Foundations of statistical natural language processing
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Recognition of Human Activity through Hierarchical Stochastic Learning
PERCOM '03 Proceedings of the First IEEE International Conference on Pervasive Computing and Communications
Topics Identification Based on Event Sequence Using Co-occurrence Words
NLDB '08 Proceedings of the 13th international conference on Natural Language and Information Systems: Applications of Natural Language to Information Systems
Data Stream Prediction Using Incremental Hidden Markov Models
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
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Estimation of topology of probabilistic models provides us with an important technique for many statistical language processing tasks. In this investigation, we propose a new topology estimation method for Hierarchical Hidden Markov Model (HHMM) that generalizes Hidden Markov Model (HMM) in a hierarchical manner. HHMM is a stochastic model which has powerful description capability compared to HMM, but it is hard to estimate HHMM topology because we have to give an initial hierarchy structure in advance on which HHMM depends. In this paper we propose a recursive estimation method of HHMM submodels by using frequent similar subsequence sets. We show some experimental results to see the effectiveness of our method.