Comparison of Bayesian learning and conjugate gradient descent training of neural networks
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University of Pretoria
Abstract
Neural networks are used in various fields to make predictions about the future value of a time series, or about the class membership of a given object. For the network to be effective, it needs to be trained on a set of training data combined with the expected results. Two aspects to keep in mind when considering a neural network as a solution, are the required training time and the prediction accuracy. This research compares the classification accuracy of conjugate gradient descent neural networks and Bayesian learning neural networks. Conjugate gradient descent networks are known for their short training times, but are not very consistent and results are heavily dependant on initial training conditions. Bayesian networks are slower, but much more consistent. The two types of neural networks are compared, and some attempts are made to combine their strong points in order to achieve shorter training times while maintaining a high classification accuracy. Bayesian learning outperforms the gradient descent methods by almost 1%, while the hybrid method achieves results between those of Bayesian learning and gradient descent. The drawback of the hybrid method is that there is no speed improvement above that of Bayesian learning.
Description
Dissertation (MEng (Electronics))--University of Pretoria, 2001.
Keywords
Neural networks, Bayesian neural networks, Sampled optimisation, Bayesian learning, UCTD
Sustainable Development Goals
Citation
Nortje, W 2001, Comparison of Bayesian learning and conjugate gradient descent training of neural networks, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/29327 >