| bibtype |
M -
Monography Chapter
|
| ARLID |
0342820 |
| utime |
20240103193500.1 |
| mtime |
20100513235959.9 |
| title
(primary) (eng) |
Efficient Feature Subset Selection and Subset Size Optimization |
| specification |
| page_count |
23 s. |
| book_pages |
524 |
|
| serial |
| ARLID |
cav_un_epca*0342819 |
| ISBN |
978-953-7619-90-9 |
| title
|
Pattern Recognition, Recent Advances |
| page_num |
75-98 |
| publisher |
| place |
Vukovar, Croatia |
| name |
In-Teh |
| year |
2010 |
|
| editor |
|
|
| keyword |
dimensionality reduction |
| keyword |
pattern recognition |
| keyword |
machine learning |
| keyword |
feature selection |
| keyword |
optimization |
| keyword |
subset search |
| keyword |
classification |
| author
(primary) |
| ARLID |
cav_un_auth*0101197 |
| name1 |
Somol |
| name2 |
Petr |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept (eng) |
Department of Pattern Recognition |
| department (cz) |
RO |
| department (eng) |
RO |
| 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*0101171 |
| name1 |
Novovičová |
| name2 |
Jana |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0101182 |
| name1 |
Pudil |
| name2 |
Pavel |
| full_dept (cz) |
Rozpoznávání obrazu |
| full_dept |
Department of Pattern Recognition |
| department (cz) |
RO |
| department |
RO |
| institution |
UTIA-B |
| full_dept |
Department of Pattern Recognition |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| source |
|
| cas_special |
| project |
| project_id |
1M0572 |
| agency |
GA MŠk |
| ARLID |
cav_un_auth*0001814 |
|
| project |
| project_id |
2C06019 |
| agency |
GA MŠk |
| country |
CZ |
| ARLID |
cav_un_auth*0216518 |
|
| project |
| project_id |
GA102/08/0593 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0239567 |
|
| project |
| project_id |
GA102/07/1594 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0228611 |
|
| research |
CEZ:AV0Z10750506 |
| abstract
(eng) |
A broad class of decision-making problems can be solved by learning approach. This can be a feasible alternative when neither an analytical solution exists nor the mathematical model can be constructed. In these cases the required knowledge can be gained from the past data which form the so-called learning or training set. Then the formal apparatus of statistical pattern recognition can be used to learn the decision-making. The first and essential step of statistical pattern recognition is to solve the problem of feature selection (FS) or more generally dimensionality reduction (DR). The chapter summarizes the state of art in feature selection, addressing key topics including: FS categorization, FS criteria, FS search strategies, FS stability. |
| reportyear |
2011 |
| RIV |
BD |
| permalink |
http://hdl.handle.net/11104/0185446 |
| arlyear |
2010 |
| mrcbU63 |
cav_un_epca*0342819 Pattern Recognition, Recent Advances 978-953-7619-90-9 75 98 Vukovar, Croatia In-Teh 2010 |
| mrcbU67 |
Herout A. 340 |
|