Natarajan dimension

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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]

Definition

Let be a set of functions from a set to a set . shatters a set if there exist two functions such that

  • For every .
  • For every , there exists a function such that

for all and for all .

The Natarajan dimension of H is the maximal cardinality of a set shattered by .

It is easy to see that if , the Natarajan dimension collapses to the Vapnik–Chervonenkis dimension.

Shalev-Shwartz and Ben-David [3] present comprehensive material on multi-class learning and the Natarajan dimension, including uniform convergence and learnability. Recently, Cohen et al[4][5] showed that the Natarajan dimension is the dominant term governing agnostic multi-class PAC learnability.

References

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