We model the problem of identifying a good algorithm from data as a statistical learning problem. Our framework captures several state-of-the-art empirical and theoretical approaches to the problem, ...

4.3 GA-BP-based multi-source data processing model. The GA-BP-based multi-source data processing model combines the BP neural network model and the GA model (Jiang et al., 2024; Liu et al., 2023; ...

Architecture: A Gaussian copula model is fitted to the data, capturing the statistical dependencies between variables. Data Split: The copula model is trained on 80% of the data, with 20% held out for ...

The success of convolutional neural networks (CNNs) benefits from the stacking of convolutional layers, which improves the model’s receptive field for image data but also causes a decrease in ...

Researchers trained the WBM using data from the Apple Heart and Movement Study, which has more than 160,000 participants who ...

where, b is the probability of success, a represents the probability that any selected data point is an outlier, and r is the number of points used to generate the model (2 and 3 for a line and plane, ...