Relative deviation learning bounds and generalization with unbounded loss functions

We present an extensive analysis of relative deviation bounds, including detailed proofs of two-sided inequalities and their implications. We also give detailed proofs of two-sided generalization bounds that hold in the general case of unbounded loss functions, under the assumption that a moment of...

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Vydáno v:	Annals of mathematics and artificial intelligence Ročník 85; číslo 1; s. 45 - 70
Hlavní autoři:	Cortes, Corinna, Greenberg, Spencer, Mohri, Mehryar
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Cham Springer International Publishing 01.01.2019 Springer Springer Nature B.V
Témata:	Analysis Artificial Intelligence Complex Systems Computer Science Deviation Expected values Inequality Machine learning Mathematics Probability Random variables Relative deviation bounds Unbounded regression Machine learning Importance weighting Generalization bounds Learning theory 97R40 Unbounded loss functions
ISSN:	1012-2443, 1573-7470
On-line přístup:	Získat plný text
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