Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains.

Theoretical Interpretations and Applications of Radial Basis Function Networks / Blanzieri, Enrico. - ELETTRONICO. - (2003).

Theoretical Interpretations and Applications of Radial Basis Function Networks

Blanzieri, Enrico
2003-01-01

Abstract

Medical applications usually used Radial Basis Function Networks just as Artificial Neural Networks. However, RBFNs are Knowledge-Based Networks that can be interpreted in several way: Artificial Neural Networks, Regularization Networks, Support Vector Machines, Wavelet Networks, Fuzzy Controllers, Kernel Estimators, Instanced-Based Learners. A survey of their interpretations and of their corresponding learning algorithms is provided as well as a brief survey on dynamic learning algorithms. RBFNs' interpretations can suggest applications that are particularly interesting in medical domains.
2003
Trento, Italia
Università degli Studi di Trento. DEPARTMENT OF INFORMATION AND COMMUNICATION TECHNOLOGY
Theoretical Interpretations and Applications of Radial Basis Function Networks / Blanzieri, Enrico. - ELETTRONICO. - (2003).
Blanzieri, Enrico
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/358870
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