  In this paper I address the practical concern of predicting how much training
data is sufficient for a statistical language learning system. First, I briefly
review earlier results and show how these can be combined to bound the expected
accuracy of a mode-based learner as a function of the volume of training data.
I then develop a more accurate estimate of the expected accuracy function under
the assumption that inputs are uniformly distributed. Since this estimate is
expensive to compute, I also give a close but cheaply computable approximation
to it. Finally, I report on a series of simulations exploring the effects of
inputs that are not uniformly distributed. Although these results are based on
simplistic assumptions, they are a tentative step toward a useful theory of
data requirements for SLL systems.
