Functional radial basis function networks

There has been recently a lot of interest for functional data analysis [1] and extensions of well-known methods to functional inputs (clustering algorithm [2], non-parametric models [3], MLP [4]). The main motivation of these methods is to benefit from the enforced inner structure of the data. This paper presents how functional data can be used with RBFN, and how the inner structure of the former can help designing the network.

• Type: Conference
• Year: 2004
• Where: ESANN
• Authors: Nicolas Delannay, Fabrice Rossi, Brieuc Conan-Guez, Michel Verleysen
• Keywords: ESANN 2004, ESANN 2007, Functional Data, Functional Data Analysis, Inner Structure