Incremental Construction of Structured Hidden Markov Models
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 798-802.
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.
@inproceedings{ijcai2007p128,
title = {Incremental Construction of Structured Hidden Markov Models},
author = {Galassi, Ugo and Giordana, Attilio and Saitta, Lorenza},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {798--802},
year = {2007},
url = {https://ijcai.org/proceedings/2007/128},
}
TY - CPAPER TI - Incremental Construction of Structured Hidden Markov Models AU - Galassi, Ugo AU - Giordana, Attilio AU - Saitta, Lorenza T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 798 EP - 802 UR - https://ijcai.org/proceedings/2007/128 ER -Copied
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