  We analyze completely the convergence speed of the \emph{batch learning
algorithm}, and compare its speed to that of the memoryless learning algorithm
and of learning with memory. We show that the batch learning algorithm is never
worse than the memoryless learning algorithm (at least asymptotically). Its
performance \emph{vis-a-vis} learning with full memory is less clearcut, and
depends on certain probabilistic assumptions.
