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

被引：0

作者：

Wang, Fuyan ^{[1
]}

Chen, Min ^{[2
]}

Zhang, Yiping ^{[3
]}

Zhao, Qin ^{[1
]}

机构：

[1] Kunming Univ, Informat & Technol Coll, Kunming, Peoples R China

[2] Yunnan Police Coll, Informat Secur Coll, Kunming, Peoples R China

[3] Yunnan Police Coll, Dept Technol & Sci, Kunming, Peoples R China

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON LOGISTICS, ENGINEERING, MANAGEMENT AND COMPUTER SCIENCE (LEMCS 2015) | 2015年 / 117卷

关键词：

Similarity measure; Context modeling; Amazing measure; description length; COMPRESSION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1797 / 1800

页数：4

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