  In this paper we propose a multi-task linear classifier learning problem
called D-SVM (Dictionary SVM). D-SVM uses a dictionary of parameter covariance
shared by all tasks to do multi-task knowledge transfer among different tasks.
We formally define the learning problem of D-SVM and show two interpretations
of this problem, from both the probabilistic and kernel perspectives. From the
probabilistic perspective, we show that our learning formulation is actually a
MAP estimation on all optimization variables. We also show its equivalence to a
multiple kernel learning problem in which one is trying to find a re-weighting
kernel for features from a dictionary of basis (despite the fact that only
linear classifiers are learned). Finally, we describe an alternative
optimization scheme to minimize the objective function and present empirical
studies to valid our algorithm.
