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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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發(fā)表于 2025-3-21 19:28:56 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Active Media Technology
期刊簡稱9th International Co
影響因子2023Tetsuya Yoshida,Gang Kou,Ning Zhong
視頻videohttp://file.papertrans.cn/145/144233/144233.mp4
發(fā)行地址Up-to-date results.State-of-the-art research.Fast-track conference proceedings
學科分類Lecture Notes in Computer Science
圖書封面Titlebook: Active Media Technology; 9th International Co Tetsuya Yoshida,Gang Kou,Ning Zhong Conference proceedings 2013 Springer International Publis
影響因子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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書目名稱Active Media Technology影響因子(影響力)




書目名稱Active Media Technology影響因子(影響力)學科排名




書目名稱Active Media Technology網(wǎng)絡(luò)公開度




書目名稱Active Media Technology網(wǎng)絡(luò)公開度學科排名




書目名稱Active Media Technology被引頻次




書目名稱Active Media Technology被引頻次學科排名




書目名稱Active Media Technology年度引用




書目名稱Active Media Technology年度引用學科排名




書目名稱Active Media Technology讀者反饋




書目名稱Active Media Technology讀者反饋學科排名




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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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