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Titlebook: Active Media Technology; 9th International Co Tetsuya Yoshida,Gang Kou,Ning Zhong Conference proceedings 2013 Springer International Publis

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期刊全称Active Media Technology
期刊简称9th International Co
影响因子2023Tetsuya Yoshida,Gang Kou,Ning Zhong
视频video
发行地址Up-to-date results.State-of-the-art research.Fast-track conference proceedings
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Active Media Technology; 9th International Co Tetsuya Yoshida,Gang Kou,Ning Zhong Conference proceedings 2013 Springer International Publis
影响因子This book constitutes the refereed proceedings of the 9th International Conference on Active Media Technology, AMT 2013, held in Maebashi, Japan, in October 2013. The 26 revised full papers presented together with 2 short papers, 16 workshop papers, and 12 special session papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on active computer systems, interactive systems, and application of AMT based systems; active media machine learning and data mining techniques; AMT for semantic web, social networks, and cognitive foundations. Additionally, the main topic of the workshop papers is: intelligence for strategic foresight; and for the special session papers: technologies and theories of narrative; evolutionary computation and its application; and intelligent media search techniques.
Pindex Conference proceedings 2013
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书目名称Active Media Technology影响因子(影响力)




书目名称Active Media Technology影响因子(影响力)学科排名




书目名称Active Media Technology网络公开度




书目名称Active Media Technology网络公开度学科排名




书目名称Active Media Technology被引频次




书目名称Active Media Technology被引频次学科排名




书目名称Active Media Technology年度引用




书目名称Active Media Technology年度引用学科排名




书目名称Active Media Technology读者反馈




书目名称Active Media Technology读者反馈学科排名




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Xiaohui Bei,Ning Chen,Shengyu Zhangs of9 differential topology and mixture distribution, the combined pattern recognition by VSF-Network is explained. Through an experiment, both the incremental learning capability and the pattern recognition with pattern combination are shown.
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Toward Model Theory with Data Valuesme patterns in its clustering process. We propose a suffix tree based algorithm that allows biclusters to have inconsistencies in at most k contiguous column. This can reveals previously undiscoverable biclusters. Our algorithm still has tractable run time with this additional feature.
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