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Generalized Radial Basis Function Networks Trained with Instance Based Learning for Data Mining of Symbolic Data

Domenii publicaţii > Ştiinţe informatice + Tipuri publicaţii > Articol în revistã ştiinţificã

Autori: S. Papadimitriou, S. Mavroudi, L. Vladutu and A. Bezerianos

Editorial: Kluwer Academic Publishers, Applied Intelligence, 16, p.223-234, 2002.


The application of neural networks to domains involving prediction
and classification of symbolic data requires a reconsideration and
a careful definition of the concept of distance between patterns.
Traditional distances are inadequate to access the differences
between the symbolic patterns. This work proposes the utilization
of a statistically extracted distance measure in the context of
Generalized Radial Basis Function (GRBF) networks. The main
properties of the GRBF networks are retained in the new metric
space. The regularization potential of these networks can be
realized with this type of distance. Furthermore, the recent
engineering of neural networks offers effective solutions for
learning smooth functionals that lie on high dimensional spaces.

Cuvinte cheie: neural network learning,data mining, symbolic data classification, radial basis functions, heuristic learning