Controlling complexity of RBF networks by similarity
Rückert U, Eickhoff R (2007)
In: ESANN. 181-186.
Konferenzbeitrag
| Veröffentlicht | Englisch
Autor*in
Rückert, UlrichUniBi;
Eickhoff, Ralf
Abstract / Bemerkung
Using radial basis function networks for function approximation
tasks suffers from unavailable knowledge about an adequate network
size. In this work, a measuring technique is proposed which can control the
model complexity and is based on the correlation coefficient between two
basis functions. Simulation results show good performance and, therefore,
this technique can be integrated in the RBF training procedure.
Erscheinungsjahr
2007
Titel des Konferenzbandes
ESANN
Seite(n)
181-186
Page URI
https://pub.uni-bielefeld.de/record/2289160
Zitieren
Rückert U, Eickhoff R. Controlling complexity of RBF networks by similarity. In: ESANN. 2007: 181-186.
Rückert, U., & Eickhoff, R. (2007). Controlling complexity of RBF networks by similarity. ESANN, 181-186.
Rückert, Ulrich, and Eickhoff, Ralf. 2007. “Controlling complexity of RBF networks by similarity”. In ESANN, 181-186.
Rückert, U., and Eickhoff, R. (2007). “Controlling complexity of RBF networks by similarity” in ESANN 181-186.
Rückert, U., & Eickhoff, R., 2007. Controlling complexity of RBF networks by similarity. In ESANN. pp. 181-186.
U. Rückert and R. Eickhoff, “Controlling complexity of RBF networks by similarity”, ESANN, 2007, pp.181-186.
Rückert, U., Eickhoff, R.: Controlling complexity of RBF networks by similarity. ESANN. p. 181-186. (2007).
Rückert, Ulrich, and Eickhoff, Ralf. “Controlling complexity of RBF networks by similarity”. ESANN. 2007. 181-186.
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Open Access
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