In the theory of Probably Approximately Correct Machine Learning, the Natarajan dimension characterizes the complexity of learning a set of functions, generalizing from the Vapnik-Chervonenkis dimension for boolean functions to multi-class functions. Originally introduced as the Generalized Dimension by Natarajan,[1] it was subsequently renamed the Natarajan Dimension by Haussler and Long.[2]
Shalev-Shwartz and Ben-David [3] present comprehensive material on multi-class learning and the Natarajan dimension, including uniform convergence and learnability.