Lensometer
发表于 2025-3-21 19:07:41
书目名称Discovery Science影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0281042<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=BK0281042<br><br> <br><br>书目名称Discovery Science网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0281042<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=BK0281042<br><br> <br><br>书目名称Discovery Science被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0281042<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=BK0281042<br><br> <br><br>书目名称Discovery Science年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0281042<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=BK0281042<br><br> <br><br>书目名称Discovery Science读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0281042<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=BK0281042<br><br> <br><br>
苦恼
发表于 2025-3-21 23:01:29
Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototypexisting software, whereas more complex classifiers would require costly software adaptations. In order to predict a time series of instances, we construct a meta classification layer. We then evaluate our model on the data of 180 locomotive tours by leave one out classification. The results show tha
Militia
发表于 2025-3-22 01:44:24
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极小
发表于 2025-3-22 08:13:00
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规范要多
发表于 2025-3-22 10:34:53
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LIEN
发表于 2025-3-22 13:26:32
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LIEN
发表于 2025-3-22 18:18:32
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negotiable
发表于 2025-3-22 22:39:37
https://doi.org/10.1007/978-3-658-01919-8of focusing on attribute subset selection, we explore an alternative promising approach consisting of using all available textual information. The problem of bug-fix time estimation is then mapped to a text categorization problem. We consider a multi-topic Supervised Latent Dirichlet Allocation (.)
COLON
发表于 2025-3-23 02:00:17
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刚开始
发表于 2025-3-23 09:22:52
Second-Order-Faktorenanalyse (SFA)r law and the number of edges increase as a function of time. Therefore, we discuss a sequential sampling method with forgetting factor to sample the evolving ego network stream. This method captures the most active and recent nodes from the network while preserving the tie strengths between them an