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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Veröffentlicht in:	Annals of mathematics and artificial intelligence Jg. 85; H. 1; S. 45 - 70
Hauptverfasser:	Cortes, Corinna, Greenberg, Spencer, Mohri, Mehryar
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.01.2019 Springer Springer Nature B.V
Schlagworte:	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
Online-Zugang:	Volltext
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