paper on stochastic grammars and resulting architectures

[Eric Mjolsness]

The following paper is available by ftp and www.


        Symbolic Neural Networks Derived from
          Stochastic Grammar Domain Models

                  Eric Mjolsness

Abstract:

Starting with a statistical domain model in the form of a
stochastic grammar, one can derive neural network architectures
with some of the expressive power of a semantic network and also
some of the pattern recognition and learning capabilities of 
more conventional neural networks. For example in this paper
a new version of the "Frameville" architecture, and in particular
its objective function and constraints, is derived from a
stochastic grammar schema.  Possible optimization dynamics
for this architecture, and relationships to other recent
architectures such as Bayesian networks and variable-binding
networks, are also discussed.


URL's for Web access:

ftp://cs.ucsd.edu/pub/emj/papers/ucsd.TR.CS95-437.ps
ftp://cs.ucsd.edu/pub/emj/papers/ucsd.TR.CS95-437.ps.Z
ftp://cs.ucsd.edu/pub/emj/papers/ucsd.TR.CS95-437.ps.gz
(or indirectly from http://www-cse.ucsd.edu/users/emj)

ftp instructions:
unix% ftp cs.ucsd.edu
Name: anonymous
Password: (use your e-mail address)
ftp> cd /pub/emj/papers
ftp> bin
ftp> get ucsd.TR.CS95-437.ps.Z (or ucsd.TR.CS95-437.ps.gz)
ftp> bye
unix% uncompress ucsd.TR.CS95-437.ps.Z (or gunzip ucsd.TR.CS95-437.ps.gz)