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.
2020-01-01

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.
2020
978-3-030-24358-6
978-3-030-24359-3
File in questo prodotto:
File Dimensione Formato  
Model+Selection+and+Error+Estimation+in+.pdf

accesso chiuso

Descrizione: Monografia
Tipologia: Documento in versione editoriale
Dimensione 2.17 MB
Formato Adobe PDF
2.17 MB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/1032189
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact