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Noise Addition

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.

In the figure above, the dependence of r2t,e   and r2e0,en on various levels of added noise An  is shown for three networks of different size and generalization capability. Curve A (good generalization): 400 data points, 15 hidden neurons, curve B (medium generalization): 200 data points, 38 hidden neurons, curve C (poor generalization): 100 data points, 70 hidden neurons.

Last Update: 2006-Jšn-17