ML Anthology

Matching State-Based Sequences with Rich Temporal Aspects

Aihua Zheng, Jixin Ma, Jin Tang, Bin Luo
AAAI 2012 pp. 2463-2464
doi:10.1609/AAAI.V26I1.8413 /aaai/2012/zheng2012aaai-matching/

Abstract

A General Similarity Measurement (GSM), which takes into account of both non-temporal and rich temporal aspects including temporal order, temporal duration and temporal gap, is proposed for state-sequence matching. It is believed to be versatile enough to subsume representative existing measurements as its special cases.

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Cite

Text

Zheng et al. "Matching State-Based Sequences with Rich Temporal Aspects." AAAI Conference on Artificial Intelligence, 2012. doi:10.1609/AAAI.V26I1.8413

Markdown

[Zheng et al. "Matching State-Based Sequences with Rich Temporal Aspects." AAAI Conference on Artificial Intelligence, 2012.](https://mlanthology.org/aaai/2012/zheng2012aaai-matching/) doi:10.1609/AAAI.V26I1.8413

BibTeX

@inproceedings{zheng2012aaai-matching,
  title     = {{Matching State-Based Sequences with Rich Temporal Aspects}},
  author    = {Zheng, Aihua and Ma, Jixin and Tang, Jin and Luo, Bin},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2012},
  pages     = {2463-2464},
  doi       = {10.1609/AAAI.V26I1.8413},
  url       = {https://mlanthology.org/aaai/2012/zheng2012aaai-matching/}
}