Ensemble-based Hybrid Optimization of Bayesian Neural Networks and
  Traditional Machine Learning Algorithms
Peiwen Tan
This research introduces a novel methodology for optimizing Bayesian Neural
Networks (BNNs) by synergistically integrating them with traditional machine
learning algorithms such as Random Forests (RF), Gradient Boosting (GB), and
Support Vector Machines (SVM). Feature integration solidifies these results by
emphasizing the second-order conditions for optimality, including stationarity
and positive definiteness of the Hessian matrix. Conversely, hyperparameter
tuning 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.