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- W19372657 abstract "With respect to linear and other classical statistical models, connexionnist ones offer interesting potentialities, despite their more difficult learning. One of the encountered difficulties consists in setting a number of meta-parameters, for example linked to the complexity of the model (number of units in a hidden layer, etc.). Often, there is no other choice than testing several possible values and selecting the best one according to a predefined criterion. There is thus a need for efficient methods for the evaluation of the model performances, coping with the constraints from real-world applications (in particular the limited number of available data). This paper presents some resampling methods to be used in the context of nonlinear (including connectionist) modeling, and compares their advantages and drawbacks in terms of performances and complexity." @default.
- W19372657 created "2016-06-24" @default.
- W19372657 creator A5009959388 @default.
- W19372657 creator A5083632408 @default.
- W19372657 date "2003-01-01" @default.
- W19372657 modified "2023-09-24" @default.
- W19372657 title "LE TEST DES METHODES NEURONALES : COMMENT UTILISER LES TECHNIQUES DE REECHANTILLONNAGE ?" @default.
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- W19372657 hasPublicationYear "2003" @default.
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