A New Similarity Measure for the Context Quantization based on the Statistic Counting Model

DOI

10.2991/lemcs-15.2015.362How to use a DOI?

Keywords

Similarity measure; Context modeling; Amazing measure; description length

Abstract

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.

Copyright

© 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/).

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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  -