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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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发表于 2025-3-21 16:06:06 | 显示全部楼层 |阅读模式
书目名称Discovery Science
副标题20th International C
编辑Akihiro Yamamoto,Takuya Kida,Tetsuji Kuboyama
视频video
丛书名称Lecture Notes in Computer Science
图书封面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
关键词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

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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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