ML Anthology

Learning Complex Event Descriptions by Abstraction

Ugo Galassi, Attilio Giordana, Lorenza Saitta, Marco Botta
IJCAI 2005 pp. 1600-1601
/ijcai/2005/galassi2005ijcai-learning/

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. 1

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Text

Galassi et al. "Learning Complex Event Descriptions by Abstraction." International Joint Conference on Artificial Intelligence, 2005.

Markdown

[Galassi et al. "Learning Complex Event Descriptions by Abstraction." International Joint Conference on Artificial Intelligence, 2005.](https://mlanthology.org/ijcai/2005/galassi2005ijcai-learning/)

BibTeX

@inproceedings{galassi2005ijcai-learning,
  title     = {{Learning Complex Event Descriptions by Abstraction}},
  author    = {Galassi, Ugo and Giordana, Attilio and Saitta, Lorenza and Botta, Marco},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2005},
  pages     = {1600-1601},
  url       = {https://mlanthology.org/ijcai/2005/galassi2005ijcai-learning/}
}