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Information theory and neural network learning algorithms

Plumbley, MD (1993) Information theory and neural network learning algorithms In: Neural Computing Research and Applications, Proceedings of the Second Irish Neural Networks Conference, Queen's University, Belfast, Northern Ireland, 25-26 June 1992, ? - ?.

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There have been a number of recent papers on information theory and neural networks, especially in a perceptual system such as vision.� Some of these approaches are examined, and their implications for neural network learning algorithms are considered.� Existing supervised learning algorithms such as Back Propagation to minimize mean squared error can be viewed as attempting to minimize an upper bound on information loss.� By making an assumption of noise either at the input or the output to the system, unsupervised learning algorithms such as those based on Hebbian (principal component analysing) or anti-Hebbian (decorrelating) approaches can also be viewed in a similar light.� The optimization of information by the use of interneurons to decorrelate output units suggests a role for inhibitory interneurons and cortical loops in biological sensory systems.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Divisions : Surrey research (other units)
Authors :
Date : 1993
Contributors :
Orchard, G
publisherInstitute of Physics Publishing,
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:36
Last Modified : 23 Jan 2020 18:42

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