The notion of equivalent number of degrees of freedom (e.d.f.) to be usedin neural network modeling from small datasets has been introduced in Ingrassiaand Morlini (2005). It is much smaller than the total number of parameters andit does not depend on the number of input variables. We generalize our previousresults and discuss the use of the e.d.f. in the general framework of multivariatenonparametric model selection. Through numerical simulations, we also investigatethe behavior of model selection criteria like AIC, GCV and BIC/SBC, when thee.d.f. is used instead of the total number of the adaptive parameters in the model.

Equivalent number of degrees of freedom for neural networks / S., Ingrassia; Morlini, Isabella. - STAMPA. - (2007), pp. 229-236. (Intervento presentato al convegno 30th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, GfKl 2006 tenutosi a Berlin, deu nel 2006) [10.1007/978-3-540-70981-7_26].

Equivalent number of degrees of freedom for neural networks

MORLINI, Isabella
2007

Abstract

The notion of equivalent number of degrees of freedom (e.d.f.) to be usedin neural network modeling from small datasets has been introduced in Ingrassiaand Morlini (2005). It is much smaller than the total number of parameters andit does not depend on the number of input variables. We generalize our previousresults and discuss the use of the e.d.f. in the general framework of multivariatenonparametric model selection. Through numerical simulations, we also investigatethe behavior of model selection criteria like AIC, GCV and BIC/SBC, when thee.d.f. is used instead of the total number of the adaptive parameters in the model.
2007
30th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, GfKl 2006
Berlin, deu
2006
229
236
S., Ingrassia; Morlini, Isabella
Equivalent number of degrees of freedom for neural networks / S., Ingrassia; Morlini, Isabella. - STAMPA. - (2007), pp. 229-236. (Intervento presentato al convegno 30th Annual Conference of the German Classification Society (Gesellschaft fur Klassifikation) on Advances in Data Analysis, GfKl 2006 tenutosi a Berlin, deu nel 2006) [10.1007/978-3-540-70981-7_26].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/462126
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