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An Algorithm for Model Determination in a Layered Network With Non-Uniform Hidden Layer Unit Set

By Keisuke Kameyama and Yukio Kosugi


this report, a training algorithm is introduced for a similar hybrid network, which grows and prunes the model by MS adaptively. In Sec. 2, the general scheme of Set of functions in model 1 St Set of functions in mdell iu / Set of functi in model 3 P " Sto , , Complexity of the model Fig. 1. The scheme of stepwise training with model switching. The training is started in model 1 and reaches the final function of a smaller model 3 via model 2. Although the models are changed during the progress of training, the path is continuous in the functional spac

Topics: Pattern classification, Clustering, Model selection, Back-propagation, Radial Basis Functions
Year: 2007
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