Learning Complex Event Descriptions by Abstraction
Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence. Pages 1600-1600.
Abstract
The presence of long gaps dramatically increases the difficulty of detecting and characterizing complex events hidden in long sequences. In order to cope with this problem, a learning algorithm based on an abstraction mechanism is proposed: it can infer a Hierarchical Hidden Markov Model, from a learning set of sequences. The induction algorithm proceeds bottom-up, progressively coarsening the sequence granularity, and letting correlations between subsequences, separated by long gaps, naturally emerge. As a case study, the method is evaluated on an application of user profiling. The results show that the proposed algorithm is suitable for developing real applications in network security and monitoring.
@inproceedings{ijcai2005p268,
title = {Learning Complex Event Descriptions by Abstraction},
author = {Galassi, Ugo and Giordana, Attilio and Saitta, Lorenza and Botta, Marco},
booktitle = {Proceedings of the Nineteenth International Joint Conference on
Artificial Intelligence, {IJCAI-05}},
publisher = {International Joint Conferences on Artificial Intelligence},
pages = {1600--1600},
year = {2005},
url = {https://ijcai.org/proceedings/2005/268},
}
TY - CPAPER TI - Learning Complex Event Descriptions by Abstraction AU - Galassi, Ugo AU - Giordana, Attilio AU - Saitta, Lorenza AU - Botta, Marco T2 - Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, IJCAI-05 PB - International Joint Conferences on Artificial Intelligence PY - 2005 SP - 1600 EP - 1600 UR - https://ijcai.org/proceedings/2005/268 ER -Copied
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