A comparison among output codification schemes
Carlos Hernández-Espinosa, Mercedes Fernández-Redondo · 2002
We present an empirical comparison among four different schemes of coding the outputs of a multilayer feedforward networks. Results are obtained for eight different classification problems from the UCI repository machine learning databases. Our results show that the usual codification is superior to the rest in the case of using one output unit per class. However, if we use several output units per class we can obtain an improvement in the generalization performance depending on the problem and in this case the noisy codification seems to be more appropriate.