Learning With Kernel Smoothing Models and Low-Discrepancy Sampling (Articolo in rivista)

Type

• Prodotto della ricerca (Classe)
• Articolo in rivista (Classe)

Label

• Learning With Kernel Smoothing Models and Low-Discrepancy Sampling (Articolo in rivista) (literal)

Anno

• 2013-01-01T00:00:00+01:00 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

• 10.1109/TNNLS.2012.2236353 (literal)

Alternative label

• Cristiano Cervellera; Danilo Macciò (2013)
  Learning With Kernel Smoothing Models and Low-Discrepancy Sampling
  in IEEE Transactions on Neural Networks and Learning Systems
  (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

• Cristiano Cervellera; Danilo Macciò (literal)

Pagina inizio

• 504 (literal)

Pagina fine

• 509 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni

• Quartile della rivista: Q1, nel settore \"Artificial Intelligence\". (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 24 (literal)

Rivista

• IEEE Transactions on Neural Networks and Learning Systems (Rivista)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali

• 6 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo

• 3 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• 1) Institute of Intelligent Systems for Automation, National Research Council, Genova 16149, Italy; 2) Institute of Intelligent Systems for Automation, National Research Council, Genova 16149, Italy (literal)

Titolo

• Learning With Kernel Smoothing Models and Low-Discrepancy Sampling (literal)

Abstract

• This brief presents an analysis of the performance of kernel smoothing models used to estimate an unknown target function, addressing the case where the choice of the training set is part of the learning process. In particular, we consider a choice of the points at which the function is observed based on low- discrepancy sequences, which is a family of sampling methods commonly employed for efficient numerical integration. We prove that, under suitable regularity assumptions, consistency of the empirical risk minimization is guaranteed with a good rate of convergence of the estimation error, as well as the convergence of the approximation error. Simulation results confirm, in practice, the good theoretical properties given by the combination of kernel smoothing models with low-discrepancy sampling. (literal)

Prodotto di

• Monitoraggio, Controllo e ottimizzazione di impianti e sistemi complessi (SP.P06.010.001) (Modulo)
• Institute of intelligent systems for automation (ISSIA) (Istituto)

Autore CNR

• DANILO MACCIO' (Persona)
• CRISTIANO CERVELLERA (Persona)

Insieme di parole chiave

• Parole chiave di "Learning With Kernel Smoothing Models and Low-Discrepancy Sampling" (Insieme di parole chiave)

Incoming links:

Prodotto

• Institute of intelligent systems for automation (ISSIA) (Istituto)
• Monitoraggio, Controllo e ottimizzazione di impianti e sistemi complessi (SP.P06.010.001) (Modulo)

Autore CNR di

• CRISTIANO CERVELLERA (Persona)
• DANILO MACCIO' (Persona)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi

• IEEE Transactions on Neural Networks and Learning Systems (Rivista)

Insieme di parole chiave di

• Parole chiave di "Learning With Kernel Smoothing Models and Low-Discrepancy Sampling" (Insieme di parole chiave)