risky-drinking
发表于 2025-3-21 16:44:09
书目名称Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0620531<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0620531<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0620531<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0620531<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0620531<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0620531<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0620531<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0620531<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0620531<br><br> <br><br>书目名称Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0620531<br><br> <br><br>
鞭打
发表于 2025-3-21 21:50:17
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alcohol-abuse
发表于 2025-3-22 01:17:06
Mining Anomalies in Subspaces of High-Dimensional Time Series for Financial Transactional Data, yet effective nearest neighbor method. The proposed system is implemented and evaluated on both synthetic and real-world transactional data. The results indicate that our anomaly retrieval system can localize high quality anomaly candidates in seconds, making it practical to use in a production en
macabre
发表于 2025-3-22 08:01:52
AIMED-RL: Exploring Adversarial Malware Examples with Reinforcement Learningarial examples that lead machine learning models to misclassify malware files, without compromising their functionality. We implement our approach using a Distributional Double Deep Q-Network agent, adding a penalty to improve diversity of transformations. Thereby, we achieve competitive results com
Mindfulness
发表于 2025-3-22 09:59:17
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脱离
发表于 2025-3-22 13:05:03
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Adrenaline
发表于 2025-3-22 18:20:46
Time Series Forecasting with Gaussian Processes Needs Priorse within a plausible range; we design such priors through an empirical Bayes approach. We present results on many time series of different types; our GP model is more accurate than state-of-the-art time series models. Thanks to the priors, a single restart is enough the estimate the hyperparameters;
Innovative
发表于 2025-3-22 23:02:50
Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Timeapproach. Based on the same data, we achieve a ten percent improvement for the wind datasets and more than . in most cases for the solar dataset for inductive transfer learning without catastrophic forgetting. Finally, we are the first to propose zero-shot learning for renewable power forecasts. Th
Indicative
发表于 2025-3-23 02:50:49
Smurf-Based Anti-money Laundering in Time-Evolving Transaction Networksn 180M transactions involving more than 31M bank accounts, and we verify its efficiency. Finally, by a careful analysis of the suspicious motifs found, we provide a classification of smurf-like motifs into categories that shed light on how money launderers exploit geography, among other things, in t
lipoatrophy
发表于 2025-3-23 06:04:59
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