The Natarajan dimension is a concept from the field of computational learning theory, specifically concerning the capacity of a class of functions in relation to its ability to learn from empirical data. It provides a way to quantify the complexity of a hypothesis class (a set of functions or models) in terms of the number of samples needed to effectively learn that class.
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