  We consider a problem of learning kernels for use in SVM classification in
the multi-task and lifelong scenarios and provide generalization bounds on the
error of a large margin classifier. Our results show that, under mild
conditions on the family of kernels used for learning, solving several related
tasks simultaneously is beneficial over single task learning. In particular, as
the number of observed tasks grows, assuming that in the considered family of
kernels there exists one that yields low approximation error on all tasks, the
overhead associated with learning such a kernel vanishes and the complexity
converges to that of learning when this good kernel is given to the learner.
