Bayesian Optimization for Parameter Tuning of the XOR Neural Network

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...

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Bibliographic Details
Published in:arXiv.org 2017-11
Main Authors: Stewart, Lawrence, Stalzer, Mark
Format: Article
Language:English
Subjects:
Artificial neural networks
Bayesian analysis
Gaussian process
Machine learning
Mathematical models
Neural networks
Optimization
Process parameters
Tuning
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Summary: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.
ISSN:2331-8422