  Although operator-valued kernels have recently received increasing interest
in various machine learning and functional data analysis problems such as
multi-task learning or functional regression, little attention has been paid to
the understanding of their associated feature spaces. In this paper, we explore
the potential of adopting an operator-valued kernel feature space perspective
for the analysis of functional data. We then extend the Regularized Least
Squares Classification (RLSC) algorithm to cover situations where there are
multiple functions per observation. Experiments on a sound recognition problem
show that the proposed method outperforms the classical RLSC algorithm.
