Hyperparameter optimization method based on dynamic Bayesian with sliding balance mechanism in neural network for cloud computing

Journal of Cloud Computing

Advances, Systems and Applications

Table 2 Results of Ablation Experiment

Optimization Method	Results in different search progress (AP50 ± Variance)
Optimization Method	1/4	2/4	3/4	1
GP (baseline)	0.454 ± 0.010	0.459 ± 0.008	0.463 ± 0.004	0.468 ± 0.005
Dynamic	0.449 ± 0.006	0.461 ± 0.003	0.465 ± 0.007	0.470 ± 0.005
Hausdorff	0.458 ± 0.005	0.462 ± 0.003	0.467 ± 0.005	0.471 ± 0.007
Dynamic + Hausdorff	0.453 ± 0.003	0.461 ± 0.005	0.465 ± 0.002	0.477 ± 0.002