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.

*** Please note that the location of the files was changed at the beginning of
** the year, so that any copies you have of the previous instructions should be 
* discarded. The new location and instructions are given at the end of the list.


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NeuroCOLT Technical Report NC-TR-96-047:
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A Graph-theoretic Generalization of the Sauer-Shelah Lemma
by  Nicol\`o Cesa-Bianchi, University of Milan, Italy
    David Haussler, University of California, Santa Cruz, USA

Abstract:
We show a natural graph-theoretic generalization of the Sauer-Shelah
lemma.  This result is applied to bound the $\ell_{\infty}$ and $L_1$
packing numbers of classes of functions whose range is an arbitrary,
totally bounded metric space.



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NeuroCOLT Technical Report NC-TR-96-048:
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A Comparison between Cellular Encoding and Direct Encoding for Genetic
Neural Networks
by  Fr\'ed\'eric Gruau, CWI, the Netherlands
    Darrell Whitley, Colorado State University, USA

Abstract:
This paper compares the efficiency of two encoding schemes for
Artificial Neural Networks optimized by evolutionary algorithms.
Direct Encoding encodes the weights for an a~priori fixed neural
network architecture.  Cellular Encoding encodes both weights and the
architecture of the neural network.  In previous studies, Direct
Encoding and Cellular Encoding have been used to create neural networks
for balancing 1 and 2 poles attached to a cart on a fixed track.   The
poles are balanced by a controller that push the cart to the left or
the right.  In some cases velocity information about the pole and cart
is provided as an input;  in other cases the network must learn to
balance a single pole without velocity information.  A careful study of
the behavior of these systems suggests that it is possible to balance a
single pole with velocity information as an input and without learning
to compute the velocity.  A new fitness function is introduced that
forces ANN to compute the velocity.  By using this new fitness function
and tuning the syntactic constraints used with cellular encoding, we
achieve a tenfold speedup over our previous study and solve a more
difficult problem:  balancing two poles when no information about the
velocity is provided as input.


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

The Report NC-TR-96-001 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-96-001.ps.Z
ftp> bye
% zcat nc-tr-96-001.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-96-002-title.ps.Z
nc-tr-96-002-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-96-002*
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/neural/neurocolt.html

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


Best wishes
John Shawe-Taylor