Disperse
发表于 2025-3-21 16:06:06
书目名称Discovery Science影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0281060<br><br> <br><br>书目名称Discovery Science影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0281060<br><br> <br><br>书目名称Discovery Science网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0281060<br><br> <br><br>书目名称Discovery Science网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0281060<br><br> <br><br>书目名称Discovery Science被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0281060<br><br> <br><br>书目名称Discovery Science被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0281060<br><br> <br><br>书目名称Discovery Science年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0281060<br><br> <br><br>书目名称Discovery Science年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0281060<br><br> <br><br>书目名称Discovery Science读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0281060<br><br> <br><br>书目名称Discovery Science读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0281060<br><br> <br><br>
colony
发表于 2025-3-21 21:19:24
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.
滴注
发表于 2025-3-22 03:29:14
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别炫耀
发表于 2025-3-22 05:03:15
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侵略主义
发表于 2025-3-22 08:52:24
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法官
发表于 2025-3-22 13:28:54
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.
法官
发表于 2025-3-22 17:33:25
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Adherent
发表于 2025-3-23 00:22:51
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muscle-fibers
发表于 2025-3-23 03:11:43
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Flatter
发表于 2025-3-23 06:02:53
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..