| bibtype |
J -
Journal Article
|
| ARLID |
0307496 |
| utime |
20240103190001.0 |
| mtime |
20080516235959.9 |
| title
(primary) (eng) |
Knowledge Uncertainty and Composed Classifier |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0305643 |
| ISSN |
1998-0140 |
| title
|
International Journal of Circuits, Systems and Signal Processing |
| volume_id |
1 |
| volume |
2 (2007) |
| page_num |
101-105 |
|
| title
(cze) |
Neurčitost znalostí a složený klasifikátor |
| keyword |
Boosting architecture |
| keyword |
contextual modelling |
| keyword |
composed classifier |
| keyword |
knowledge management, |
| keyword |
knowledge |
| keyword |
uncertainty |
| author
(primary) |
| ARLID |
cav_un_auth*0101129 |
| name1 |
Klimešová |
| name2 |
Dana |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0015539 |
| name1 |
Ocelíková |
| name2 |
E. |
| country |
SK |
|
| cas_special |
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
The paper discuss the problem of wide context (temporal, spatial, local, objective, attribute oriented, relation oriented) as a tool to compensate and to decrease the uncertainty of data, classification and analytical process at all process to increase the information value of decision support. The contribution deals with a problem of creating the composed classifier with boosting architecture, whose components are composed of classifiers working with k - NN algorithm (k - th nearest neighbour). |
| abstract
(cze) |
Příspěvek je věnován problematice širokého kontextu z hlediska jeho možností kompenzovat neurčitost dat. |
| reportyear |
2008 |
| RIV |
IN |
| permalink |
http://hdl.handle.net/11104/0160247 |
| arlyear |
2007 |
| mrcbU63 |
cav_un_epca*0305643 International Journal of Circuits, Systems and Signal Processing 1998-0140 Roč. 1 č. 2 2007 101 105 |
|