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Titlebook: Discovery Science; 20th International C Akihiro Yamamoto,Takuya Kida,Tetsuji Kuboyama Conference proceedings 2017 Springer International Pu

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發(fā)表于 2025-3-21 16:06:06 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Discovery Science
副標(biāo)題20th International C
編輯Akihiro Yamamoto,Takuya Kida,Tetsuji Kuboyama
視頻videohttp://file.papertrans.cn/282/281060/281060.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Discovery Science; 20th International C Akihiro Yamamoto,Takuya Kida,Tetsuji Kuboyama Conference proceedings 2017 Springer International Pu
描述This book constitutes the proceedings of the 20th International Conference on Discovery Science, DS 2017, held in Kyoto, Japan, in October 2017, co-located with the International Conference on Algorithmic Learning Theory, ALT 2017. .The 18 revised full papers presented together with 6 short papers and 2 invited talks in this volume were carefully reviewed and selected from 42 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in topical sections on machine learning: online learning, regression, label classification, deep learning, feature selection, recommendation system; and knowledge discovery: recommendation system, community detection, pattern mining, misc..
出版日期Conference proceedings 2017
關(guān)鍵詞classification; community detection; data mining; feature selection; knowledge discovery; label classific
版次1
doihttps://doi.org/10.1007/978-3-319-67786-6
isbn_softcover978-3-319-67785-9
isbn_ebook978-3-319-67786-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer International Publishing AG 2017
The information of publication is updating

書目名稱Discovery Science影響因子(影響力)




書目名稱Discovery Science影響因子(影響力)學(xué)科排名




書目名稱Discovery Science網(wǎng)絡(luò)公開度




書目名稱Discovery Science網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Discovery Science被引頻次




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https://doi.org/10.1007/978-94-009-3387-3is paper, we propose the FTRL-DP online algorithm to address the problem of malware detection under concept drift when the behavior of malware changes over time. The experimental results show that online learning outperforms batch learning in all settings, either with or without retrainings.
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A New Adaptive Learning Algorithm and Its Application to Online Malware Detectionis paper, we propose the FTRL-DP online algorithm to address the problem of malware detection under concept drift when the behavior of malware changes over time. The experimental results show that online learning outperforms batch learning in all settings, either with or without retrainings.
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Conference proceedings 2017ll as their application in various scientific domains. The papers are organized in topical sections on machine learning: online learning, regression, label classification, deep learning, feature selection, recommendation system; and knowledge discovery: recommendation system, community detection, pattern mining, misc..
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