  We consider the problem of learning a classifier from observed functional
data. Here, each data-point takes the form of a single time-series and contains
numerous features. Assuming that each such series comes with a binary label,
the problem of learning to predict the label of a new coming time-series is
considered. Hereto, the notion of {\em margin} underlying the classical support
vector machine is extended to the continuous version for such data. The
longitudinal support vector machine is also a convex optimization problem and
its dual form is derived as well. Empirical results for specified cases with
significance tests indicate the efficacy of this innovative algorithm for
analyzing such long-term multivariate data.
