• In this introductory talk, I would like to address some important issues complimentary to those addressed in my workshop paper.

Freeman Dyson has been professor of physics at the Institute for Advanced Studies in Princeton from 1953.  

I have been there myself.

Ironically, this comes from the pen of the leaders of statistical pattern recognition.

If you want see how this applies to the ETS model, bring it up during the question period.

This is what von Neumann and Turing were engaged in during the last years of their life.  

See, however, regular workshops SSPR, ICGI, and many other.

Here is Robert Ledley’s example of the generative grammar for the two classes of chromosomes: submedian (S) and telocentric (T). A bottom-up parsing process is also illustrated by the tree.

For example, non-linear functions are not compositions of the linear ones.

The classical computational models do not satisfy these requirements, which is why both Turing and Von Neumann have proceeded to look for new models.

We use non-bold t (small Greek tau) to denote a generic transformation.

Add the corresponding class weight schemes and you have all the class representations (at this level).

The initial sites of the primitive correspond to the interface sites, while the terminal sites of the primitive correspond to the terminal sites of the supertransform.

See our main paper, the link to which is given in the workshop announcement (see the second link in it).

Scientists have not gained yet any experience with such new “measurement” processes.

How could conventional decision surfaces in Rn have been accepted as meaningful/useful class descriptions? They cannot in principle convey anything of scientific interest neither about the classes nor their elements?

Moving on to the ETS model, . . .

Only some of the features are listed.