Adaptive noise injection for input variables relevance determination View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1997

AUTHORS

Yves Grandvalet , Stéphane Canu

ABSTRACT

In this paper we consider the application of training with noise in multi-layer perceptron to input variables relevance determination. Noise injection is modified in order to penalize irrelevant features. The proposed algorithm is attractive as it requires the tuning of a single parameter. This parameter controls the penalization of the inputs together with the complexity of the model. After the presentation of the method, experimental evidences are given on simulated data sets. More... »

PAGES

463-468

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0020198

DOI

http://dx.doi.org/10.1007/bfb0020198

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1053575212


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