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This research introduces a novel methodology for optimizing Bayesian NeuralNetworks (BNNs) by synergistically integrating them with traditional machinelearning algorithms such as Random Forests (RF), Gradient Boosting (GB), andSupport Vector Machines (SVM). Feature integration solidifies these results byemphasizing the second-order conditions for optimality, including stationarityand positive definiteness of the Hessian matrix. Conversely, hyperparametertuning indicates a subdued impact in improving Expected Improvement (EI),represented by EI(x). Overall, the ensemble method stands out as a robust,algorithmically optimized approach.

Ensemble-based Hybrid Optimization of Bayesian Neural Networks and Traditional Machine Learning Algorithms