  We propose a practical Bayesian optimization method using Gaussian process
regression, of which the marginal likelihood is maximized where the number of
model selection steps is guided by a pre-defined threshold. Since Bayesian
optimization consumes a large portion of its execution time in finding the
optimal free parameters for Gaussian process regression, our simple, but
straightforward method is able to mitigate the time complexity and speed up the
overall Bayesian optimization procedure. Finally, the experimental results show
that our method is effective to reduce the execution time in most of cases,
with less loss of optimization quality.
