  Much recent research has been conducted in the area of Bayesian learning,
particularly with regard to the optimization of hyper-parameters via Gaussian
process regression. The methodologies rely chiefly on the method of maximizing
the expected improvement of a score function with respect to adjustments in the
hyper-parameters. In this work, we present a novel algorithm that exploits
notions of confidence intervals and uncertainties to enable the discovery of
the best optimal within a targeted region of the parameter space. We
demonstrate the efficacy of our algorithm with respect to machine learning
problems and show cases where our algorithm is competitive with the method of
maximizing expected improvement.
