A Gaussian potential function network with hierarchically self-organizing learning

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Deep learning in neural networks: An overview (2014, background): “…Oja, 1990; Lampinen & Oja, 1992; Rauber, Merkl, & Dittenbach, 2002; Versino & Gambardella, 1996), hierarchical Gaussian potential function networks (Lee & Kil, 1991), layer-wise UL of feature hierarchies fed into SL classifiers (Behnke, 1999, 2003a), the SelfOrganizing Tree Algorithm (SOTA)…”
Face recognition with radial basis function (RBF) neural networks (2002, methodology): “• They have more compact topology than other neural networks [20].”
High-speed face recognition based on discrete cosine transform and RBF neural networks (2005, background): “Recently, RBF neural networks have been applied in many engineeringandscientificapplications includingfacerecognition [22], [23].”
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation (2005, methodology): “To our best knowledge, this is different from all the existing sequential learning algorithms including RAN, RANEKF, HSOL [18], and the sequential growing and pruning algorithm proposed by Todorović and Stanković [19] since all of them add new neurons based on their novelty to the individual insta”
Dynamic fuzzy neural networks-a novel approach to function approximation (2000, background): “2) Hierarchical Learning: The concept of “hierarchical learning” was first presented in [ 9 ].”