  This article deals with the problem of functional classification for
L2-valued random covariates when some of the covariates may have missing or
unobservable fragments. Here, it is allowed for both the training sample as
well as the new unclassified observation to have missing fragments in their
functional covariates. Furthermore, unlike most previous results in the
literature, where covariate fragments are typically assumed to be missing
completely at random, we do not impose any such assumptions here. Given the
observed segments of the curves, we construct a kernel-type classifier which is
quite straightforward to implement in practice. We also establish the strong
consistency of the proposed classifier and provide some numerical examples to
assess its performance in practice.
