The notion of Stability [1–3] allows to answer a fundamental question in learning theory: which are the properties that a learning algorithm A should fulfill in order to achieve good generalization performance? Stability answers this question in a very intuitive way: if A selects similar models, even if the training data are (slightly) modified, then we can be confident that the learning algorithm is stable.

Algorithmic Stability Theory

Oneto L.
2020

Abstract

The notion of Stability [1–3] allows to answer a fundamental question in learning theory: which are the properties that a learning algorithm A should fulfill in order to achieve good generalization performance? Stability answers this question in a very intuitive way: if A selects similar models, even if the training data are (slightly) modified, then we can be confident that the learning algorithm is stable.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/1032189
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