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Title

Neural learning methods yielding functional invariance

AuthorsRuiz de Angulo, Vicente ; Torras, Carme
KeywordsNeural learning
Regularization
Functional invariance
Input noise addition
Weight-decay
Issue Date2004
PublisherElsevier
CitationTheoretical Computer Science 320(1): 111-121 (2004)
AbstractThis paper investigates the functional invariance of neural network learning methods incorporating a complexity reduction mechanism, such as a regularizer. By functional invariance we mean the property of producing functionally equivalent minima as the size of the network grows, when the smoothing parameters are fixed. We study three different principles on which functional invariance can be based, and try to delimit the conditions under which each of them acts. We find out that, surprisingly, some of the most popular neural learning methods, such as weight-decay and input noise addition, exhibit this interesting property.
DescriptionA preliminary version of this work was presented at the International Conference on Arti cial Neural Networks (ICANN'01), Vienna, August 2001.
Publisher version (URL)http://dx.doi.org/10.1016/j.tcs.2004.03.046
URIhttp://hdl.handle.net/10261/30547
DOI10.1016/j.tcs.2004.03.046
ISSN0304-3975
Appears in Collections:(IRII) Artículos
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