Incremental Construction of Structured Hidden Markov Models
Abstract
This paper presents an algorithm for inferring a Structured Hidden Markov Model (S-HMM) from a set of sequences. The S-HMMs are a sub-class of the Hierarchical Hidden Markov Models and are well suited to problems of process/user profiling. The learning algorithm is unsupervised, and follows a mixed bottom-up/top-down strategy, in which elementary facts in the sequences (motifs) are progressively grouped, thus building up the abstraction hierarchy of a S-HMM, layer after layer. The algorithm is validated on a suite of artificial datasets, where the challenge for the learning algorithm is to reconstruct the model that generated the data. Then, an application to a real problem of molecular biology is briefly described.
Cite
Text
Galassi et al. "Incremental Construction of Structured Hidden Markov Models." International Joint Conference on Artificial Intelligence, 2007.Markdown
[Galassi et al. "Incremental Construction of Structured Hidden Markov Models." International Joint Conference on Artificial Intelligence, 2007.](https://mlanthology.org/ijcai/2007/galassi2007ijcai-incremental/)BibTeX
@inproceedings{galassi2007ijcai-incremental,
title = {{Incremental Construction of Structured Hidden Markov Models}},
author = {Galassi, Ugo and Giordana, Attilio and Saitta, Lorenza},
booktitle = {International Joint Conference on Artificial Intelligence},
year = {2007},
pages = {798-803},
url = {https://mlanthology.org/ijcai/2007/galassi2007ijcai-incremental/}
}