paper available on grammatical inference using RNN

[Marco Maggini]

FTP-host: ftp-dsi.ing.unifi.it
FTP-filename: /pub/tech-reports/noisy-gram.ps.Z

The following paper, which has been submitted to NEURAP-95 is available
via anonymous FTP at the above location.
The paper is 8 pages long (about 200Kb).

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          Learning Regular Grammars From Noisy Examples Using 
                       Recurrent Neural Networks

                   M. Gori, M. Maggini, and G. Soda

                Dipartimento di Sistemi e Informatica
                       Universita' di Firenze
            Via di Santa Marta 3 - 50139 Firenze - Italy
           Tel. +39 (55) 479.6265 - Fax +39 (55) 479.6363
      E-mail : {marco,maggini,giovanni}@mcculloch.ing.unifi.it
                WWW: http:/www-dsi.ing.unifi.it/neural

                                ABSTRACT

   Many successful results have recently been reported concerning
   the application of recurrent neural networks to the induction of
   simple finite state grammars. These results can be used to explore
   the computational capabilities of neural models applied to symbolic
   tasks. Many insights have been given on the links between the
   continuous dynamics of a recurrent neural network and the symbolic
   rules that we want to learn.

   However, so far, the advantages of dynamical adaptive models and
   the related gradient-driven learning techniques with respect to classical
   symbolic inference algorithms have not been clearly shown. In this paper,
   we explore a class of inductive inference problems that seems to be very
   well-suited for optimization-based learning algorithms. Bearing in mind
   the idea of optimal rather than perfect solution, we explore how
   optimality criteria can help a successful development of the learning
   process when some of the examples are erroneously labeled.
  
   Some experimental results show that neural network-based learning
   algorithms favor the development of the ``simplest'' solutions,
   thus eliminating most of the exceptions that arise when dealing with
   erroneous examples.

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The paper can be accessed and printed as follows: 

% ftp ftp-dsi.ing.unifi.it  (150.217.11.10)
Name: anonymous
password: your full email address
ftp> cd /pub/tech-reports
ftp> binary
ftp> get noisy-gram.ps.Z
ftp> bye
% uncompress noisy-gram.ps.Z
% lpr noisy-gram.ps