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Plotting a learner's generalization performance against the training set sizeresults in a so-called learning curve. This tool, providing insight in thebehavior of the learner, is also practically valuable for model selection,predicting the effect of more training data, and reducing the computationalcomplexity of training. We set out to make the (ideal) learning curve conceptprecise and briefly discuss the aforementioned usages of such curves. Thelarger part of this survey's focus, however, is on learning curves that showthat more data does not necessarily leads to better generalization performance.A result that seems surprising to many researchers in the field of artificialintelligence. We point out the significance of these findings and conclude oursurvey with an overview and discussion of open problems in this area thatwarrant further theoretical and empirical investigation.

A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance