Technical Report Series in Neural and Computational Learning

[John Shawe-Taylor]

The European Community ESPRIT Working Group in Neural and Computational 
Learning Theory (NeuroCOLT) has produced a set of new Technical Reports
available from the remote ftp site described below. They cover topics in
real valued complexity theory, computational learning theory, and analysis
of the computational power of continuous neural networks.  Abstracts are
included for the titles.


The following technical report has been updated to include information 
about the system described in NC-TR-97-038:
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NeuroCOLT Technical Report NC-TR-97-028:
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Overview of Learning Systems produced by NeuroCOLT Partners
by NeuroCOLT Partners

Abstract:
This NeuroCOLT Technical Report documents a number of systems that
have been produced withing the NeuroCOLT partnership.  It only includes
a summary of each system together with pointers to where the system is
located and more information about its performance and design can
be found. 


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NeuroCOLT Technical Report NC-TR-97-038:
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Using Computational Learning Strategies as a Tool for Combinatorial 
   Optimization
by Andreas Birkendorf and Han Ulrich Simon, Universit"at Dortmund,
   Germany

Abstract:
In this paper, we describe how a basic strategy from computational
learning theory can be used to attach a class of NP-hard combinatorial
optimization problems.  It turns out that the learning strategy can be 
used as an iterative booster: given a solution to the combinatorial
problem, we will start an efficient simulation of a learning algorithm
which as a ``good chance'' to output an improved solution.  This 
boosting technique is a new and surprisingly simple application of an
existing learning strategy.  It yields a novel heuristic approach to
attach NP-hard optimization problems.  It does not apply to each
combinatorial problem, but we are able to exactly formalize some
sufficient conditions.  The new technique applies, for instance, to
the problems of minimizing a deterministic finite automaton relative 
to a given domain, the analogous problem for ordered binary decision 
diagrams, and to graph colouring.


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NeuroCOLT Technical Report NC-TR-97-039:
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A Unifying Framework for Invariant Pattern Recognition
by Jeffrey Wood and John Shawe-Taylor, Royal Holloway, University of 
   London, UK

Abstract:
We introduce a group-theoretic model of invariant pattern recognition, the
{\em Group Representation Network}.  We show that many standard invariance
techniques can be viewed as GRNs, including the DFT power spectrum, higher
order neural network and fast translation-invariant transform.


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***************** ACCESS INSTRUCTIONS ******************

The Report NC-TR-97-001 can be accessed and printed as follows 

% ftp ftp.dcs.rhbnc.ac.uk  (134.219.96.1)
Name: anonymous
password: your full email address
ftp> cd pub/neurocolt/tech_reports
ftp> binary
ftp> get nc-tr-97-001.ps.Z
ftp> bye
% zcat nc-tr-97-001.ps.Z | lpr -l

Similarly for the other technical reports.

Uncompressed versions of the postscript files have also been
left for anyone not having an uncompress facility. 

In some cases there are two files available, for example,
nc-tr-97-002-title.ps.Z
nc-tr-97-002-body.ps.Z
The first contains the title page while the second contains the body 
of the report. The single command,
ftp> mget nc-tr-97-002*
will prompt you for the files you require.

A full list of the currently available Technical Reports in the 
Series is held in a file `abstracts' in the same directory.

The files may also be accessed via WWW starting from the NeuroCOLT 
homepage:

http://www.dcs.rhbnc.ac.uk/research/compint/neurocolt

or directly to the archive:
ftp://ftp.dcs.rhbnc.ac.uk/pub/neurocolt/tech_reports


Best wishes
John Shawe-Taylor