Paper

[Ganesh Mani]

The following paper is available for ftp from the repository at Ohio State.
Please backpropagate comments (and errors!) to ganesh at cs.wisc.edu.

-Ganesh Mani

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Learning by Gradient Descent in Function Space

Ganesh Mani
Computer Sciences Dept.
Unviersity of Wisconsin---Madison
ganesh at cs.wisc.edu

Abstract

Traditional connectionist networks have homogeneous nodes
wherein each node executes the same function.  Networks where each node
executes a different function can be used to achieve efficient
supervised learning. A modified back-propagation algorithm
for such networks, which performs gradient descent in ``function space,''
is presented and its advantages are discussed.  The benefits of the 
suggested paradigm include faster learning and ease of interpretation 
of the trained network.

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The following can be used to ftp the paper.


unix> ftp cheops.cis.ohio-state.edu          # (or ftp 128.146.8.62)
Name (cheops.cis.ohio-state.edu:): anonymous
Password (cheops.cis.ohio-state.edu:anonymous): neuron
ftp> cd pub/neuroprose
ftp> type binary
ftp> get
(remote-file)  mani.function-space.ps.Z
(local-file) mani.function-space.ps.Z
ftp> quit
unix> uncompress  mani.function-space.ps.Z
unix> lpr -P(your_local_postscript_printer)  mani.function-space.ps