Thermometer coding for multilayer perceptron learning on continuous mapping problems

It is shown that a multilayer perceptron can learn highly nonlinear continuous mappings more easily if the target values are thermometer-coded. Because of the similarity between sigmoidal functions and thermometer coding, a network does not need more hidden nodes to produce thermometer-coded target values when more output nodes are added to the original structure. Furthermore, the weights for the hidden layers of a network, which are trained by thermometer-coded target values, can be used in the initialization of a network which is then trained by the original target values. The reason why such a two-staged learning is possible is discussed. Experiments on synthetic data sets show that using thermometer-coded target values improves the learning performance of a network and that conversion to a single output network is more efficient and gives better results