A New Similarity Measure for the Context Quantization based on the Statistic Counting Model
- 10.2991/lemcs-15.2015.362How to use a DOI?
- Similarity measure; Context modeling; Amazing measure; description length
In this paper, one new similarity measure which holds better mathematical description is given and discussed in details. The increment of the amazing measure, which denotes the similarity measure between two count vectors is discussed in this paper and its corresponding properties are also explained. We also give the analysis and the proof to explain the efficiency of the proposed similarity measure. The experimental results indicate that when using the proposed similarity measure , the corresponding results for different applications can be optimized.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Fuyan Wang AU - Min Chen AU - Yiping Zhang AU - Qin Zhao PY - 2015/07 DA - 2015/07 TI - A New Similarity Measure for the Context Quantization based on the Statistic Counting Model BT - Proceedings of the International Conference on Logistics, Engineering, Management and Computer Science PB - Atlantis Press SP - 1779 EP - 1782 SN - 1951-6851 UR - https://doi.org/10.2991/lemcs-15.2015.362 DO - 10.2991/lemcs-15.2015.362 ID - Wang2015/07 ER -