On the Uniform Convergence of Consistent Confidence Measures

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Summary

It is shown that finite VC-dimension is sufficient for guaranteeing the consistency of confidence measures produced by empirically consistent classifiers and implies that one can calibrate confidence measuresproduced by any existing algorithms with monotonic functions, and still get the same generalization guarantee on consistency.

Type
preprint
Published
2015-06-09
Cited by
0
References
23
Access
Open access

Keywords

Interpretability, Consistency (knowledge bases), Measure (data warehouse), Confidence interval, Computer science

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