Tech report available

[Tony Robinson]

Here is the summary of a tech report which demonstates that the error
propagation algorithm is not limited to weighted-sum type nodes, but can
be used to train radial-basis-function type nodes and others.  Send me some
email if you would like a copy.

Tony.

P.S.   If you asked for a copy of my/our last paper, I've taken the
       liberty of sending you a hard copy of this one as well.

Thank you for replying to ajr at dsl.eng.cam.ac.uk not connectionists at ...

`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'`'
         Generalising the Nodes of the Error Propagation Network
               CUED/F-INFENG/TR.25
                   A J Robinson, M Niranjan, F Fallside
               Cambridge University Engineering Department
                  Trumpington Street, Cambridge, England
                       email: ajr at uk.ac.cam.eng.dsl
                             1 November 1988

Gradient descent has been used with much success to train connectionist
models in the form of the Error Propagation Network (Rumelhart Hinton and
Williams, 1986).  In these nets the output of a node is a non-linear
function of the weighted sum of the activations of other nodes.  This type
of node defines a hyper-plane in the input space, but other types of nodes
are possible.  For example, the Kanerva Model (Kanerva 1984), the Modified
Kanerva Model (Prager and Fallside 1988), networks of Spherical Graded
Units (Hanson and Burr, 1987), networks of Localised Receptive Fields
(Moody and Darken, 1988) and the method of Radial Basis Functions (Powell,
1985; Broomhead and Lowe 1988) all use nodes which define volumes in the
input space.  Niranjan and Fallside (1988) summarise these and compare the
class boundaries formed by this family of networks with feed-forward
networks and nearest neighbour classifiers.  This report shows that the
error propagation algorithm can be used to train general types of node.
The example of a gaussian node is given and this is compared with other
connectionist models for the problem of recognition of steady state vowels
from multiple speakers.