平项山
发表于 2025-3-25 05:15:45
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Initiative
发表于 2025-3-25 11:34:38
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HPA533
发表于 2025-3-25 12:44:34
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分离
发表于 2025-3-25 17:59:13
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神刊
发表于 2025-3-25 22:21:36
Over-Fitting in Model Selection with Gaussian Process Regression,which allows flexible customization of the GP to the problem at hand. An oft-overlooked issue that is often encountered in the model process is over-fitting the model selection criterion, typically the marginal likelihood. The over-fitting in machine learning refers to the fitting of random noise pr
ostensible
发表于 2025-3-26 01:34:24
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exhibit
发表于 2025-3-26 04:35:18
Anomaly Detection from Kepler Satellite Time-Series Data,s. Windowed mean division normalization is presented as a method to transform non-linear data to linear data. Modified Z-score, general extreme studentized deviate, and percentile rank algorithms were applied to initially detect anomalies. A refined windowed modified Z-score algorithm was used to de
Locale
发表于 2025-3-26 11:31:30
Prediction of Insurance Claim Severity Loss Using Regression Models,nal data used for this research work is obtained from Allstate insurance company which consists of 116 categorical and 14 continuous predictor variables. We implemented Linear regression, Random forest regression (RFR), Support vector regression (SVR) and Feed forward neural network (FFNN) for this
guardianship
发表于 2025-3-26 15:11:47
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幸福愉悦感
发表于 2025-3-26 19:43:55
Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks, work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulner