Initialization of adaptive parameters in density networks

Initialization of adaptive parameters in neural networks is of crucial importance to the speed of convergence of the learning procedure. Methods of initialization for the density networks are reviewed and two new methods, based on decision trees and dendrograms, presented. These two methods were applied in the Feature Space Mapping framework to artificial and real world datasets. Results show superiority of the dendrogram-based method including rotation.