Title: Bayesian Optimization for Parameter Tuning of the XOR Neural Network,
Abstract: When applying Machine Learning techniques to problems, one must select model
parameters to ensure that the system converges but also does not become stuck
at the objective function's local minimum. Tuning these parameters becomes a
non-trivial task for large models and it is not always apparent if the user has
found the optimal parameters. We aim to automate the process of tuning a Neural
Network, (where only a limited number of parameter search attempts are
available) by implementing Bayesian Optimization. In particular, by assigning
Gaussian Process Priors to the parameter space, we utilize Bayesian
Optimization to tune an Artificial Neural Network used to learn the XOR
function, with the result of achieving higher prediction accuracy.