Many works have shown that strong connections relate learning from examples to regularization techniques for ill-posed inverse problems. Nevertheless by now there was no formal evidence neither that learning from examples could be seen as an inverse problem nor that theoretical results in learning theory could be independently derived using tools from regularization theory. In this paper we provide a positive answer to both questions. Indeed, considering the square loss, we translate the learningproblem in the language of regularization theory and show that consistency results and optimal regularization parameter choice can be derived by the discretization of the corresponding inverse problem.

Learning, Regularization and Ill-Posed Inverse Problems / Rosasco, L.; Caponnetto, A.; DE VITO, Ernesto; De Giovannini, U.; Odone, F.. - STAMPA. - 17:(2005), pp. 1145-1152. (Intervento presentato al convegno Eighteenth Annual Conference on Neural Information Processing Systems tenutosi a Vancouver (Canada) nel 13-16 Dicembre 2004).

Learning, Regularization and Ill-Posed Inverse Problems

DE VITO, Ernesto;
2005

Abstract

Many works have shown that strong connections relate learning from examples to regularization techniques for ill-posed inverse problems. Nevertheless by now there was no formal evidence neither that learning from examples could be seen as an inverse problem nor that theoretical results in learning theory could be independently derived using tools from regularization theory. In this paper we provide a positive answer to both questions. Indeed, considering the square loss, we translate the learningproblem in the language of regularization theory and show that consistency results and optimal regularization parameter choice can be derived by the discretization of the corresponding inverse problem.
2005
Eighteenth Annual Conference on Neural Information Processing Systems
Vancouver (Canada)
13-16 Dicembre 2004
17
1145
1152
Rosasco, L.; Caponnetto, A.; DE VITO, Ernesto; De Giovannini, U.; Odone, F.
Learning, Regularization and Ill-Posed Inverse Problems / Rosasco, L.; Caponnetto, A.; DE VITO, Ernesto; De Giovannini, U.; Odone, F.. - STAMPA. - 17:(2005), pp. 1145-1152. (Intervento presentato al convegno Eighteenth Annual Conference on Neural Information Processing Systems tenutosi a Vancouver (Canada) nel 13-16 Dicembre 2004).
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

Licenza Creative Commons
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/597952
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? ND
social impact