Comparison of prototype selection algorithms used in construction of neural networks learned by SVD - Jankowski, Norbert (Zielona Góra: Uniwersytet Zielonogórski)
Radial basis function networks (RBFNs) or extreme learning machines (ELMs) can be seen as linear combinations of kernel functions (hidden neurons). Kernels can be constructed in random processes like in ELMs, or the positions of kernels can be initialized by a random subset of training vectors, or kernels can be constructed in a (sub-)learning process (sometimes by k-means, for example). We found that kernels constructed using prototype selection algorithms provide very accurate and stable solutions.
Keywords: radial basis function network; extreme learning machines; kernel methods; prototype; prototype selection; machine learning; k nearest neighbours