TR's available (via ftp)

[B. Fritzke]

Hi there,

I just have placed two short papers in the Neuroprose Archive at 
cheops.cis.ohio-state.edu (128.146.8.62) in the directory pub/neuroprose.

The files are:
   fritzke.cell_structures.ps.Z (to be presented at ICANN-91 Helsinki)
   fritzke.clustering.ps.Z      (to be presented at IJCNN-91 Seattle)

They both deal with a new self-organizing network based on the model of
Kohonen. The first one describes the model and the second one concentrates
one an application.

     LET IT GROW -- SELF-ORGANIZING FEATURE MAPS WITH  
           PROBLEM  DEPENDENT CELL STRUCTURE
                       Bernd FRITZKE

Abstract:  The self-organizing  feature  maps  introduced  by  T.
Kohonen  use  a  cell array of fixed size and structure.  In many
cases this array is not able to model a given signal distribution
properly.   We present a method to construct two-dimensional cell
structures during a self-organization process which are specially
adapted  to  the  underlying  distribution: Starting with a small
number of cells new cells are added successively. Thereby  signal
vectors according to the (usually not explicitly known) probabil-
ity distribution are used to determine where to insert or  delete
cells  in  the  current  structure. This process leads to problem
dependent cell structures which model the given distribution with
arbitrary high accuracy.


     UNSUPERVISED CLUSTERING WITH GROWING CELL STRUCTURES
                        Bernd FRITZKE

Abstract: A Neural Network model is presented which  is  able  to
detect  clusters   of   similar   patterns.   The patterns are n-
dimensional real number vectors according to an  unknown   proba-
bility   distribution P(X).  By  evaluating  sample  vectors  ac-
cording  to P(X) a two-dimensional cell  structure  is  gradually
built  up  which  models  the distribution.  Through  removal  of
cells corresponding to areas with  low  probability  density  the
structure  is  then  split  into several  disconnected  substruc-
tures. Each of them identifies one cluster of  similar  patterns.
Not  only  the number of  clusters  is determined but also an ap-
proximation of the probability distribution inside each  cluster.
The  accuracy  of the cluster description is  increased  linearly
with the number of evaluated sample vectors.

Enjoy,
Bernd 

Bernd Fritzke ---------->  e-mail: fritzke at immd2.informatik.uni-erlangen.de
University of Erlangen, CS IMMD II, Martensstr. 3,  8520 Erlangen (Germany)