  Plotting a learner's generalization performance against the training set size
results in a so-called learning curve. This tool, providing insight in the
behavior of the learner, is also practically valuable for model selection,
predicting the effect of more training data, and reducing the computational
complexity of training. We set out to make the (ideal) learning curve concept
precise and briefly discuss the aforementioned usages of such curves. The
larger part of this survey's focus, however, is on learning curves that show
that more data does not necessarily leads to better generalization performance.
A result that seems surprising to many researchers in the field of artificial
intelligence. We point out the significance of these findings and conclude our
survey with an overview and discussion of open problems in this area that
warrant further theoretical and empirical investigation.
