Model complexity control and regularization play a crucial role in statistical learning theory and also for problems in system identification. This text discusses the potential of the issue of regularization in identification of Hammerstein systems in the context of primal-dual kernel machines and Least Squares Support Vector Machines (LS-SVMs) and proposes an extension of the Hammerstein class to finite order Volterra series and methods resulting in structure detection.