Learning curves exhibit a diversity of behaviors such as phase transition. However, the understanding of learning curves is still extremely limited, and existing theories can give the impression that without empirical studies (e.g., cross validation), one can probably do nothing more than qualitative interpretations. In this note, we propose a theory of learning curves based on the idea of reducing learning problems to hypothesis-testing ones. This theory provides a simple approach that is potentially useful for predicting and interpreting (a diversity of) learning curve behaviors qualitatively and quantitatively, and it applies to finite training sample size and finite learning machine and for learning situations not necessarily within the Bayesian framework. We illustrate the results by examining some exponential learning curve behaviors observed in Cohn and Tesauro (1992)'s experiment.