| bibtype |
V -
Research Report
|
| ARLID |
0083328 |
| utime |
20240103184229.8 |
| mtime |
20070618235959.9 |
| title
(primary) (eng) |
Selection of Most Informative Variables in Statistical Pattern Recognition |
| publisher |
| place |
Plzeň |
| name |
UWB |
| pub_time |
2007 |
|
| specification |
|
| edition |
| name |
MATEO -The European Network of Mechatronics Centres and Industrial Controllers |
|
| title
(cze) |
Výběr nejinformativnějších proměnných ve statistickém rozpoznávání |
| keyword |
feture selection |
| author
(primary) |
| ARLID |
cav_un_auth*0101182 |
| name1 |
Pudil |
| name2 |
Pavel |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101197 |
| name1 |
Somol |
| name2 |
Petr |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101093 |
| name1 |
Haindl |
| name2 |
Michal |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| cas_special |
| project |
| project_id |
MAT-12-C4 |
| agency |
GA MMR |
| country |
CZ |
| ARLID |
cav_un_auth*0228022 |
|
| project |
| project_id |
1M0572 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0001814 |
|
| project |
| project_id |
2C06019 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0216518 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
The research report gives an overview of feature selection techniques in statistical pattern recognition with particular emphasis to methods developed by the researchers participating in MATEO Centre of Mechatronics project. Besides discussing the advances in methodology it attempts to put them into a taxonomical framework. The methods discussed include the latest variants of the optimal algorithms, enhanced sub-optimal techniques and the simultaneous semi-parametric probability density function modelling and feature space selection method. Some related issues are illustrated on real data by means of the Feature Selection Toolbox software. |
| abstract
(cze) |
Výzkumná zpráva obsahuje přehled metod výběru příznaků ve statistickém rozpoznávání s důrazem na metody vyvinuté výzkumníky projektu MATEO Centre of Mechatronics. Diskutované metody zahrnují nejnovější verze optimalizačních algoritmů, sub-optimální techniky a modelování simultánní semi-parametrické pravděpodobnostní hustoty a metody výběru příznaků. Metody jsou ilustrované na reálných datech pomocí programu Feature Selection Toolbox. |
| reportyear |
2008 |
| RIV |
BD |
| permalink |
http://hdl.handle.net/11104/0146600 |
| arlyear |
2007 |
| mrcbU10 |
2007 |
| mrcbU10 |
Plzeň UWB |
|