Yet another neural network, the radial basis function (RBF) network was used as a function approximation to compare against the MLP and SVM models. The parameter settings for the RBF have not been optimised so far. I simply ran it against the MLP/SVM on the same cross validation data. The results can be obtained from the following two graphics:

Mean Absolute Error, MLP vs. SVM vs. RBFRoot Mean Squared Error, MLP vs. SVM vs. RBF

The script for the above graphics is online.

At the moment I’m running some simulations to determine the size of the hidden layer of the RBF network, as this seems to be the most important parameter. The matlab implementation of the RBF network also takes some time to incrementally add neurons up to a maximum number (user-specified).