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→Summation layer: Changed title from Pattern to summation to reflect common nomenclature |
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This layer contains one neuron for each case in the training data set. It stores the values of the predictor variables for the case along with the target value. a hidden neuron computes the Euclidean distance of the test case from the neuron’s center point and then applies the RBF kernel function using the sigma values.
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For PNN networks there is one pattern neuron for each category of the target variable. The actual target category of each training case is stored with each hidden neuron; the weighted value coming out of a hidden neuron is fed only to the pattern neuron that corresponds to the hidden neuron’s category. The pattern neurons add the values for the class they represent
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