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【答案】应助回帖
★ ★ ★ ★ ★
感谢参与，应助指数 +1
why6116(lazy锦溪代发): 金币+5 2016-10-17 16:45:13
lazy锦溪: LS-EPI+1 2016-10-17 16:45:16
恭喜已被CPCI收录 具体如下
A new Similarity Measure for the Context Quantization based on the Statistic Counting Model
作者:Wang, FY (Wang, Fuyan)[ 1 ] ; Chen, M (Chen, Min)[ 2 ] ; Zhang, YP (Zhang, Yiping)[ 3 ] ; Zhao, Q (Zhao, Qin)[ 1 ]
编者:Yingying, S; Guiran, C; Zhen, L
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON LOGISTICS, ENGINEERING, MANAGEMENT AND COMPUTER SCIENCE (LEMCS 2015)
丛书: Advances in Intelligent Systems Research
卷: 117 页: 1797-1800
出版年: 2015
会议名称
会议: International Conference on Logistics Engineering, Management and Computer Science (LEMCS)
会议地点: Shenyang, PEOPLES R CHINA
会议日期: JUL 29-31, 2015
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.
关键词
作者关键词:Similarity measure; Context modeling; Amazing measure; description length
KeyWords Plus:COMPRESSION
作者信息
通讯作者地址: Chen, M (通讯作者)
Yunnan Police Coll, Informat Secur Coll, Kunming, Peoples R China.
地址:
[ 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
电子邮件地址:4626747@qq.com; zhangyiping_001@163.com; minkeychen@sina.cn; painkiller5230@qq.com
出版商
ATLANTIS PRESS, 29 AVENUE LAVMIERE, PARIS, 75019, FRANCE
类别 / 分类
研究方向:Computer Science; Business & Economics; Operations Research & Management Science
Web of Science 类别:Computer Science, Artificial Intelligence; Management; Operations Research & Management Science
文献信息
文献类型 roceedings Paper
语种:English
入藏号: WOS:000373107000366
ISBN:978-94-6252-102-5
ISSN: 1951-6851
其他信息
IDS 号: BE5OK
Web of Science 核心合集中的 "引用的参考文献": 12
Web of Science 核心合集中的 "被引频次": 0 |
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