Deep feature extraction for representing and classifying time series cases: towards an interpretable approach in haemodialysis

Risultato della ricerca: Contributo alla conferenza › Contributo in Atti di Convegno › peer review

Lingua originale	Inglese
Pagine	417-420
Numero di pagine	4
Stato di pubblicazione	Pubblicato - 1 gen 2020
Evento	International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020 - Miami Durata: 1 gen 2020 → …

???event.eventtypes.event.conference???	International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020
Città	Miami
Periodo	1/01/20 → …

Accesso al documento

@conference{096e66f6fa0f4e79b1481f6308134322,

title = "Deep feature extraction for representing and classifying time series cases: towards an interpretable approach in haemodialysis",

author = "GIORGIO LEONARDI and Stefania MONTANI and Manuel STRIANI",

year = "2020",

month = jan,

day = "1",

language = "English",

pages = "417--420",

note = "International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020 ; Conference date: 01-01-2020",

}

Risultato della ricerca: Contributo alla conferenza › Contributo in Atti di Convegno › peer review

TY - CONF

T1 - Deep feature extraction for representing and classifying time series cases: towards an interpretable approach in haemodialysis

AU - LEONARDI, GIORGIO

AU - MONTANI, Stefania

AU - STRIANI, Manuel

PY - 2020/1/1

Y1 - 2020/1/1

UR - https://iris.uniupo.it/handle/11579/112800

M3 - Paper

SP - 417

EP - 420

T2 - International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020

Y2 - 1 January 2020

ER -