  In most papers establishing consistency for learning algorithms it is assumed
that the observations used for training are realizations of an i.i.d. process.
In this paper we go far beyond this classical framework by showing that support
vector machines (SVMs) essentially only require that the data-generating
process satisfies a certain law of large numbers. We then consider the
learnability of SVMs for $\a$-mixing (not necessarily stationary) processes for
both classification and regression, where for the latter we explicitly allow
unbounded noise.
