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Table of Contents Multivariate Data Basic Knowledge Validation of Models Noise Addition | |
See also: generalization |
Generalization is a very important aspect when setting up non-linear models (especially when using neural networks). In order to create well-performing models, one has to check the generalization ability of the model. In this respect, generalization can be seen as noise-immunity: the model should not adapt itself to any noise present in the system. This aspect leads us to the idea that the generalization behavior of a model can be tested by adding increasingly more noise to the training data and checking the stability of the model .
In order to perform the generalization test, we need two measures:
These figures are calculated at various levels of noise. The trends
of these two figures as noise increases indicate the generalisation of
the network. A network which performs well will show a decreasing r2t,e,
since the increasing noise level will not be reflected in the estimated
function. On the other hand, the value of r2e0,en
should stay almost constant, since the estimated function of a noisy data
set will not differ much from the estimated function of the original data
set. The situation is just a mirror image when overfitting occurs: the
parameter r2t,e will be almost constant and
the value of r2e0,en will decrease with increasing
noise, since the networks tend to adjust themselves to the noisy sample
data, neglecting the underlying trend of the data.
Last Update: 2006-Jän-17