Radial Basis Function Neural Networks(RBF NN) are a tool largely used for regression problems. The principal drawback of this kind of predictive tool is that the optimization problem solved to train the network can be non-convex. On the other hand Canonical Duality Theory offers a powerful procedure to reformulate general non-convex problems in dual forms so that it is possible to find optimal solutions and to get deep insights into the nature of the challenging problems. By combining the canonical duality theory with the RBF NN, this paper presents a potentially useful method for solving challenging problems in real-world applications.

Radial Basis Function Neural Networks (RBF NN) are a tool largely used for regression problems. The principal drawback of this kind of predictive tool is that the optimization problem solved to train the network can be non-convex. On the other hand Canonical Duality Theory offers a powerful procedure to reformulate general non-convex problems in dual forms so that it is possible to find optimal solutions and to get deep insights into the nature of the challenging problems. By combining the canonical duality theory with the RBF NN, this paper presents a potentially useful method for solving challenging problems in real-world applications. © Springer-Verlag Berlin Heidelberg 2013.

Canonical Duality for Radial Basis Neural Networks / Latorre, Vittorio; David Yang, Gao. - 212:(2013), pp. 1189-1197. [10.1007/978-3-642-37502-6_139]

Canonical Duality for Radial Basis Neural Networks

LATORRE, VITTORIO;
2013

Abstract

Radial Basis Function Neural Networks(RBF NN) are a tool largely used for regression problems. The principal drawback of this kind of predictive tool is that the optimization problem solved to train the network can be non-convex. On the other hand Canonical Duality Theory offers a powerful procedure to reformulate general non-convex problems in dual forms so that it is possible to find optimal solutions and to get deep insights into the nature of the challenging problems. By combining the canonical duality theory with the RBF NN, this paper presents a potentially useful method for solving challenging problems in real-world applications.
2013
Radial Basis Function Neural Networks (RBF NN) are a tool largely used for regression problems. The principal drawback of this kind of predictive tool is that the optimization problem solved to train the network can be non-convex. On the other hand Canonical Duality Theory offers a powerful procedure to reformulate general non-convex problems in dual forms so that it is possible to find optimal solutions and to get deep insights into the nature of the challenging problems. By combining the canonical duality theory with the RBF NN, this paper presents a potentially useful method for solving challenging problems in real-world applications. © Springer-Verlag Berlin Heidelberg 2013.
radial basis functions; neural network; canonical duality
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Canonical Duality for Radial Basis Neural Networks / Latorre, Vittorio; David Yang, Gao. - 212:(2013), pp. 1189-1197. [10.1007/978-3-642-37502-6_139]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/668931
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