  This paper presents a RKHS, in general, of vector-valued functions intended
to be used as hypothesis space for multi-task classification. It extends
similar hypothesis spaces that have previously considered in the literature.
Assuming this space, an improved Empirical Rademacher Complexity-based
generalization bound is derived. The analysis is itself extended to an MKL
setting. The connection between the proposed hypothesis space and a Group-Lasso
type regularizer is discussed. Finally, experimental results, with some
SVM-based Multi-Task Learning problems, underline the quality of the derived
bounds and validate the paper's analysis.
