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Title

A deterministic algorithm that emulates learning with random weights

AuthorsRuiz de Angulo, Vicente ; Torras, Carme
KeywordsNoise
Noisy training
Regularization
Fault-tolerance
Optimization
Sensitivity
Adaptive systems
Automation
Issue Date2002
PublisherElsevier
CitationNeurocomputing 48(1): 975-1002 (2002)
AbstractThe expectation of a function of random variables can be modeled as the value of the function in the mean value of the variables plus a penalty term. Here, this penalty term is calculated exactly, and the properties of different approximations are analyzed. Then, a deterministic algorithm for minimizing the expected error of a feedforward network of random weights is presented. Given a particular feedforward network architecture and a training set, this algorithm accurately finds the weight configuration that makes the network response most resistant to a class of weight perturbations. Finally, the study of the most stable configurations of a network unravels some undesirable properties of networks with asymmetric activation functions.
Publisher version (URL)http://dx.doi.org/10.1016/S0925-2312(01)00695-6
URIhttp://hdl.handle.net/10261/30523
DOI10.1016/S0925-2312(01)00695-6
ISSN0925-2312
Appears in Collections:(IRII) Artículos
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