Common approaches to  the fitting of additive mixed models are based on
 the representation of semi-structured models as mixed models.
 We propose an alternative approach based on boosting techniques.
 Boosting originates in the machine learning community where
 it has been developed as a technique to improve classification
 procedures by combining estimates with reweighted observations.
 In linear mixed models as well as in semi-structured mixed models the
 advantage of the proposed componentwise boosting technique is
 that it is suitable for high dimensional settings where many influence
 variables are present. It allows to fit models for many
 covariates with implicit selection of relevant variables.