Generalized Radial Basis Functions were used to construct networks
that learn input-output mappings from given data. They are
developed out of a theoretical framework for approximation based
on regularization techniques and represent a class of three-layer
networks similar to backpropagation networks with one hidden
layer.
A network using Gaussian base functions was implemented and
applied to several domains. It was found to perform very well on the
two-spirals problem and on the nettalk task.
This paper explains what Generalized Radial Basis Functions are,
describes the algorithm, its implementation, and the tests that have
been conducted. It draws the conclusion that network.
implementations using Generalized Radial Basis Functions are a
successful approach for learning from examples. / Graduation date: 1991
| Identifer | oai:union.ndltd.org:ORGSU/oai:ir.library.oregonstate.edu:1957/37667 |
| Date | 27 June 1990 |
| Creators | Wettschereck, Dietrich |
| Contributors | Dietterich, Thomas G. |
| Source Sets | Oregon State University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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