identification vs. authentication problems

[Jocelyn Sietsma Penington]

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.
So, for example, if a net has been trained to classify A,B,C and D, the hidden
layer(s) will develop units sensitive to the characteristics of, say, A, C and
D.  This allows the training set to be correctly classified, but most 
completely new inputs will give the output pattern for B.

My (kludgy) response is to add another class of training data.  This contains
more patterns than any of the 'real' classes, and is as diverse and noisy
as I can make it.  I use the unary output coding convention (target output 
for the first class is (1,0,0), for the second class is (0,1,0), etc.)
and set the target for the 'unknown' class as all zeroes.

What are people's responses to this?  Does it address Francoise Fogelman's
problem of wanting to authenticate, rather than simply classify?

From: Bill Treurniet <bill at albert.dgbt.doc.ca>
>The same issue arises when classifying speech data with a network. How does 
>one reject non-speech data when the network has been trained with only clean 
>speech data?  A kludgy method that worked for me to some extent was to 
[method deleted]

>The method assumes that the actual distribution of hidden unit activations 
>for each class in the training set is unimodal; i.e., there is only one set 
>of hidden layer "features" that give rise to a particular classification. 
>There is also the issue of how to set the rejection criterion.  These issues 
>were sufficiently troubling that I do not use this method as a matter of 
>course. 

I agree with you that these are troubling issues.  In particular, I would 
expect that the first hidden layer activations could only be unimodal if
the domain is linearly separable, as this means a single layer of units has 
separated it.

Jocelyn Sietsma

Email: sietsma at LATCS1.LAT.oz.au           Address: Materials Research Laboratory
Phone: (03) 246 8660 or (03) 479 1057              PO Box 50, Melbourne 3032