Robustness

[Guido Bugmann]

Robustness is a vague but often used concept. Is there an
accepted method to determine the robustness of a NN ?

In an application of a FF Backprop net to the reproduction
of a function f(x,y) [1], we had measured the robustness in
the following way: 
After completed training, each weight was set to zero in turn
and the root mean square of the relative errors (RMS) (relative 
differences between the actual outputs of the net and the outputs
defined in the training set) was measured (The mean is over all
the examples in the training set).
In our case, the largest RMS induced by the loss of one connection
was 1600 %. We have used this "worst possible damage" as a measure 
of the (non-) robustness of the network.

[1] Bugmann, G., Lister, J.B. and von Stockar, U. (1989)
   "The standard deviation method: Data Analysis by Classical Means
   and by Neural Networks", Lab Report LRP-384/89, CRPP, Swiss Federal
   Institute of Technology, CH-1015 Lausanne.

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Guido Bugmann
NEC Fund. Res. Lab.
34 Miyukigaoka
Tsukuba, Ibaraki 305
Japan
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