Recently, the complexity control of dynamic neural models has gained significant attention. The performance of such a process depends highly on the applied definition of ‘model complexity’. On the other hand, the learning theory creates a framework to assess the learning properties of models. These properties include the required size of the training samples as well as the statistical confidence over the model. In this Letter, we apply the learning properties of two families of Radial Basis Function Networks (RBFN's) to introduce new complexity measures that reflect the learning properties of such neural model. Then, based on these complexity terms we define cost functions, which provide a balance between the training and testing performances of the model.
computational complexitycomputational learning theoryempirical risk minimization algorithmRadial Basis Function Networks