Abstract
A General Similarity Measurement (GSM), which takes into account of both non temporal and rich temporal aspects in cluding temporal order, temporal duration and temporal gap, is proposed for state sequence matching. It is believed to be versatile enough to subsume representative existing meas urements as its special cases.
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CITATION STYLE
APA
Zheng, A., Ma, J., Tang, J., & Luo, B. (2012). Matching State-Based Sequences with Rich Temporal Aspects. In Proceedings of the 26th AAAI Conference on Artificial Intelligence, AAAI 2012 (pp. 2463–2464). AAAI Press. https://doi.org/10.1609/aaai.v26i1.8413
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