identification vs. authentication problems

[Frank Smieja]

The issue of network/network system response to an unlearnt class,  or
random pattern,  was discussed in 

	F. J. Smieja & H. Muehlenbein
	Reflective Modular Neural Network Systems
(submitted to Machine Learning,  also available in the ohio state uni
NEUROPROSE archive as 'smieja.reflect.ps.Z')

as the "dog-paw" test.  For singly-operating networks the conclusion
was either tolerate it or degrade the 'real' learning by explicitly
mapping random patterns to another class output.  It was shown
that for the modular network system introduced in the paper 'garbage'
answers could be learnt-out without significant degradation to the
'real' learning.

Jocelyn Sietsma writes:
-) 
-) In my opinion (based on somewhat limited experience) feed-forward ANNs usually
-) develop a 'default output' or default class, which means most new inputs which
-) are different to the training classes will be classified in the same way.
I cannot agree with this.  The fraction of garbage classified as a
particular class depends on the distribution of the classes in the
pattern  space,  and the frequency with which each class is seen during the
learning. This determines how the pattern space is split up by the
ANN's hyperplanes,  and,  of course,  how each possible pattern in the
input space will be classified.


	-Frank Smieja

Gesellschaft fuer Mathematik und Datenverarbeitung (GMD)
GMD-FIT.KI.AS, Schloss Birlinghoven, 5205 St Augustin 1, Germany.
Tel: +49 2241-142214		email: smieja at gmd.de