Introduction to RBF networks plus Matlab package

[Mark Orr]

Announcing the availability of the following resources on the
World Wide Web at URL http://www.cns.ed.ac.uk/people/mark.html.



                Introduction to RBF Networks
                ----------------------------

    A 67 page introduction to linear feed-forward neural
    networks for supervised learning, such as radial basis
    function networks, where there is a single hidden layer
    and the only parameters that change during learning are
    the weights from the hidden units to the outputs.

    But more importantly it covers "nearly linear" networks:
    networks which, though nonlinear (because learning affects
    more than just the hidden-to-output weights) can still be
    analysed with simple mathematics (linear algebra) and which
    don't need compute intensive gradient descent methods to learn.
    This applies to RBF networks with local ridge regression or
    which use regularised forward selection to build the hidden
    layer. These techniques, in conjunction with leave-one-out or
    generalised cross-validation, are covered in detail.

    Available in PostScript or hyper-text.



                     Matlab Routines for
                     -------------------
            Subset Selection and Ridge Regression
            -------------------------------------
                  in Linear Neural Networks
                  -------------------------

    A package of Matlab routines implementing regularised or
    unregularised forward subset selection and global or local
    ridge regression in linear networks such as radial basis
    function networks. Comes with a 45 page user manual with
    plenty of examples.

    Available as a compressed unix tape archive (.tar file).



The author would like to acknowledge support from the UK Joint
Councils Initiative in Human Computer Interaction and Cognitive
Science under grant G9213375, "Designing Systems of Coupled Networks".

Mark Orr
mark at cns.ed.ac.uk