CINECA IRIS Institutional Research Information System
IRIS è l’archivio della produzione scientifica della Fondazione Bruno Kessler.
EnglishThe ability to deal with partial or uncertain information is a fundamental requirement for systems working in the real world. In many applications, it is important to learn relations between the data even when they appear incomplete or corrupted by noise. In others, it is necessary to know how to react in presence of missing or unreliable inputs, for instance when sensors fail or provide noisy measurements. In this paper reviews some techniques and algorithms proposed in the literature for dealing with incomplete data in areas related to statistical pattern recognition, density estimation, and neural networks. Particular attention is given to methods based on statistical models which allow to cope also with noisy data
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte di FBK.
| Titolo: | Dealing with Incomplete Data in Pattern Recognition |
| Autori: | |
| Data di pubblicazione: | 1997 |
| Abstract: | The ability to deal with partial or uncertain information is a fundamental requirement for systems working in the real world. In many applications, it is important to learn relations between the data even when they appear incomplete or corrupted by noise. In others, it is necessary to know how to react in presence of missing or unreliable inputs, for instance when sensors fail or provide noisy measurements. In this paper reviews some techniques and algorithms proposed in the literature for dealing with incomplete data in areas related to statistical pattern recognition, density estimation, and neural networks. Particular attention is given to methods based on statistical models which allow to cope also with noisy data |
| Handle: | http://hdl.handle.net/11582/1447 |
| Appare nelle tipologie: | 5.12 Altro |
File in questo prodotto:
http://hdl.handle.net/11582/1447
Copyright © 2021