Details of Grant 

EPSRC Reference:	GR/L19232/01
Title:	ANALYSIS OF ON-LINE LEARNING IN NEURAL NETWORKS
Principal Investigator:	Saad, Professor D
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Department:	Electronic Engineering
Organisation:	Aston University
Scheme:	Standard Research (Pre-FEC)
Starts:	01 November 1996	Ends:	31 March 1999	Value (£):	106,235
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Summary on Grant Application Form
Artificial Neural Networks have been successfully applied over the years to perform various tasks. However, in the absence of a successful theoretical analysis of neural network training for multi-layer perceptrons, most of the training methods used, as well as the setting of the training coefficients, are based on heuristic observations. One of the leading techniques in neural networks training, especially for large systems, is on-line learning of continuous functions via gradient descent in a differentiable error measure. This technique has been successfully applied in many real-world training problems and is arguably the most commonly used neural networks technique. A recent study, signalling a breakthrough in the analysis of on-line learning scenarios. This project, building directly on these developments as well as insights gained from earlier work, will focus on examining the effects of various types of noise on the learning process and the efficiency of regularisation techniques for improving network performance in on-line learning scenarios. In addition we will exploit the theoretical framework to examine analytically existing heuristic training techniques and to develop methods for improving the training process which will be demonstrated on real-world tasks.
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Organisation Website:	http://www.aston.ac.uk