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 ...

Machine learning algorithms. Machine learning depends on a number of algorithms for turning a data set into a model. Which algorithm works best depends on the kind of problem you’re solving, the ...

Machine learning uses algorithms to turn a data set into a model that can identify patterns or make predictions from new data. Which algorithm works best depends on the problem.

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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 ...

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, ...