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A stochastic configuration network based on online Bayesian optimization
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DOI：10.1016/j.neucom.2026.134281.png)
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Abstract
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• An online Bayesian parameter configuration mechanism is proposed for SCN to configure parameters online according to the current residual state, thereby improving node effectiveness. • Joint optimization of λ and r in continuous space replaces grid-based selection with flexible configuration for the hidden node to be added. • Experiments on function approximation and six benchmark datasets ver-ify the accuracy, generalization, and stability of OBO-SCN.
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