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Matching state-based sequences with rich temporal aspects

Matching state-based sequences with rich temporal aspects

Zheng, Aihua, Ma, Jixin, Tang, Jin and Luo, Bin (2012) Matching state-based sequences with rich temporal aspects. Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence. pp. 2463-2464.

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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.

Item Type: Article
Additional Information: [1] This paper was first presented at the Twenty-Sixth AAAI Conference on Artificial Intelligence (AAAI-12) held from 22 - 26 July 2012 in Toronto, Canada.
Uncontrolled Keywords: General Similarity Measurement, (GSM), temporal order, temporal duration, temporal gap
Subjects: Q Science > QA Mathematics > QA76 Computer software
Pre-2014 Departments: School of Computing & Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:24
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/9700

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