Recruitment Learning of Boolean Functions in Sparse Random Networks
Hogan, James M. & Diederich, Joachim (2001) Recruitment Learning of Boolean Functions in Sparse Random Networks. International Journal of Neural Systems, 11(6), pp. 537-559.
This work presents a new class of neural network models constrained by biological levels of sparsity and weight-precision, and employing only local weight updates. Concept learning is accomplished through the rapid recruitment of existing network knowledge – complex knowledge being realised as a combination of existing basis concepts. Prior network knowledge is here obtained through the random generation of feedforward networks, with the resulting concept library tailored through distributional bias to suit a particular target class. Learning is exclusively local – through supervised Hebbian and Winnow updates – avoiding the necessity for backpropagation of error and allowing remarkably rapid learning. The approach is demonstrated upon concepts of varying difficulty, culminating in the well-known Monks and LED benchmark problems.
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|Item Type:||Journal Article|
|Additional Information:||For more information, please refer to the journal’s website (see hypertext link) or contact the author.|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
|Copyright Owner:||Copyright 2001 World Scientific Publishing|
|Copyright Statement:||Electronic version of an article published as International Journal of Neural Systems 11(6):pp. 537-559.
[http://dx.doi.org/10.1142/S0129065701000953] © copyright World Scientific Publishing Company
|Deposited On:||20 Sep 2007 00:00|
|Last Modified:||10 Aug 2011 13:51|
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