Title :
A generalization error estimate for nonlinear systems
Author_Institution :
Tech. Univ. of Denmark, Lyngby, Denmark
fDate :
31 Aug-2 Sep 1992
Abstract :
A new estimate (GEN) of the generalization error is presented. The estimator is valid for both incomplete and nonlinear models. An incomplete model is characterized in that it does not model the actual nonlinear relationship perfectly. The GEN estimator has been evaluated by simulating incomplete models of linear and simple neural network systems. Within the linear system GEN is compared to the final prediction error criterion and the leave-one-out cross-validation technique. It was found that the GEN estimate of the true generalization error is less biased on the average. It is concluded that GEN is an applicable alternative in estimating the generalization at the expense of an increased complexity
Keywords :
generalisation (artificial intelligence); neural nets; nonlinear systems; parameter estimation; final prediction error criterion; generalization error estimate; incomplete model; leave-one-out cross-validation technique; linear system; neural network systems; nonlinear models; nonlinear systems; parameter estimation; Buildings; Computer architecture; Computer networks; Cost function; Neural networks; Noise generators; Nonlinear systems; Parameter estimation; Signal processing; Testing;
Conference_Titel :
Neural Networks for Signal Processing [1992] II., Proceedings of the 1992 IEEE-SP Workshop
Conference_Location :
Helsingoer
Print_ISBN :
0-7803-0557-4
DOI :
10.1109/NNSP.1992.253710
