Network Constructing Algorithms.

[Orjan Ekeberg]

I assume that some of the work that we have been doing would fit
well in this context too.  Based on a recurrent network, higher order units
are added automatically.  The new units become part of the recurrent set
and helps to make the training patterns fixpoints of the network.

A couple of references (in bibtex format):

@inproceedings{sans:alaoe87,
    author = {Anders Lansner and {\"O}rjan Ekeberg},
    year = 1987,
    title = {An Associative Network Solving the ``4-Bit ADDER Problem''},
    booktitle = {Proceedings of the IEEE First Annual International
        Conference on Neural Networks},
    pages = {II{-}549},
    address = {San Diego, USA},
    month = jun}

@inproceedings{sans:paris88,
    author = {{\"O}rjan Ekeberg and Anders Lansner},
    year = 1988,
    title = {Automatic Generation of Internal Representations
        in a Probabilistic Artificial Neural Network},
    booktitle = {Neural Networks from Models to Applications},
    editor = {L. Personnaz and G. Dreyfus},
    publisher = {I.D.S.E.T.},
    address = {Paris},
    pages = {178--186},
    note = {Proceedings of {nEuro}-88, The First European Conference
        on Neural Networks},
    abstract = {In a one layer feedback perceptron type network,
the connections can be viewed as coding the pairwise correlations
between activity in the corresponding units. This can then be used to
make statistical inference by means of a relaxation technique based on
bayesian inferences.

When such a network fails, it might be because the regularities are
not visible as pairwise correlations. One cure would then be to use a
different internal coding where selected higher order correlations are
explicitly represented. A method for generating this representation
automatically is reviewed and results from experiments regarding the
resulting properties is presented with a special focus on the networks
ability to generalize properly.}}


+---------------------------------+-----------------------+
+ Orjan Ekeberg              + O---O---O          +
+ Department of Computing Science +  \ /|\ /| Studies of  +
+ Royal Institute of Technology      +   O-O-O-O  Artificial +
+ S-100 44 Stockholm, Sweden      +   |/ \ /|   Neural      +
+---------------------------------+   O---O-O    Systems  +
+ EMail: orjan at bion.kth.se      + SANS-project      +
+---------------------------------+-----------------------+