Estimating an Optimal Neighborhood Size in the Spherical Self-Organizing Feature Map Year: 2008 Volume: 2 Issue: 6 1850 - 1854 Pages Authors: Alexandros Leontitsis Archana P. Sangole Abstract: This article presents a short discussion on optimum neighborhood size selection in a spherical selforganizing feature map (SOFM). A majority of the literature on the SOFMs have addressed the issue of selecting optimal learning parameters in the case of Cartesian topology SOFMs. However, the use of a Spherical SOFM suggested that the learning aspects of Cartesian topology SOFM are not directly translated. This article presents an approach on how to estimate the neighborhood size of a spherical SOFM based on the data. It adopts the L-curve criterion, previously suggested for choosing the regularization parameter on problems of linear equations where their right-hand-side is contaminated with noise. Simulation results are presented on two artificial 4D data sets of the coupled Hénon-Ikeda map. Keywords: Parameter estimation self-organizing feature maps spherical topology.