Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction (Articolo in rivista)

Type

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

Label

• Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction (Articolo in rivista) (literal)

Anno

• 2012-01-01T00:00:00+01:00 (literal)

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

• 10.1109/JSEN.2012.2192425 (literal)

Alternative label

• Saverio De Vito, Grazia Fattoruso, Matteo Pardo, Francesco Tortorella, Girolamo Di Francia (2012)
  Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction
  in IEEE sensors journal; IEEE, Institute of electrical and electronics engineers, New York (Stati Uniti d'America)
  (literal)

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

• Saverio De Vito, Grazia Fattoruso, Matteo Pardo, Francesco Tortorella, Girolamo Di Francia (literal)

Pagina inizio

• 3215 (literal)

Pagina fine

• 3224 (literal)

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

• 12 (literal)

Rivista

• IEEE sensors journal (Rivista)

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

• 11 (literal)

Note

• ISI Web of Science (WOS) (literal)
• Google Scholar (literal)
• Scopus (literal)

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

• Italian National Agency for New Technologies, Energy and Sustainable Development (ENEA), Portici Research Center, Portici 80055, Italy DAEIMI Department, University of Cassino and Lazio Meridionale, Cassino 03043, Italy Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche, Genova 16149, Italy (literal)

Titolo

• Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction (literal)

Abstract

• Semi-supervised learning is a promising research area aiming to develop pattern recognition tools capable to exploit simultaneously the benefits from supervised and unsupervised learning techniques. These can lead to a very efficient usage of the limited number of supervised samples achievable in many artificial olfaction problems like distributed air quality monitoring. We believe it can also be beneficial in addressing another source of limited knowledge we have to face when dealing with real world problems: concept and sensor drifts. In this paper we describe the results of two artificial olfaction investigations that show semi-supervised learning techniques capabilities to boost performance of state-of-the art classifiers and regressors. The use of semi-supervised learning approach resulted in the effective reduction of drift-induced performance degradation in long-term on-field continuous operation of chemical multisensory devices. (literal)

Editore

• IEEE, Institute of electrical and electronics engineers (Editore)

Prodotto di

• Algoritmi e strumenti per il calcolo ad alte prestazioni ed il Grid computing nel trattamento di dati 3D in ambito bioinformatico (INT.P02.008.002) (Modulo)
• Istituto di matematica applicata e tecnologie informatiche \"Enrico Magenes\" (IMATI) (Istituto)

Autore CNR

• MATTEO PARDO (Persona)

Insieme di parole chiave

• Parole chiave di "Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction" (Insieme di parole chiave)

Incoming links:

Prodotto

• Istituto di matematica applicata e tecnologie informatiche \"Enrico Magenes\" (IMATI) (Istituto)
• Algoritmi e strumenti per il calcolo ad alte prestazioni ed il Grid computing nel trattamento di dati 3D in ambito bioinformatico (INT.P02.008.002) (Modulo)

Autore CNR di

• MATTEO PARDO (Persona)

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

• IEEE sensors journal (Rivista)

Editore di

• IEEE, Institute of electrical and electronics engineers (Editore)

Insieme di parole chiave di

• Parole chiave di "Semi-Supervised Learning Techniques in Artificial Olfaction: A Novel Approach to Classification Problems and Drift Counteraction" (Insieme di parole chiave)