| bibtype |
C -
Conference Paper (international conference)
|
| ARLID |
0410332 |
| utime |
20240103182206.6 |
| mtime |
20060210235959.9 |
| ISBN |
3-540-67704-6 |
| title
(primary) (eng) |
Combining multiple classifiers in probabilistic neural networks |
| publisher |
| place |
Berlin |
| name |
Springer |
| pub_time |
2000 |
|
| specification |
|
| edition |
| name |
Lecture Notes in Computer Science. |
| volume_id |
1857 |
|
| serial |
| title
|
Multiple Classifier Systems |
| page_num |
157-166 |
| editor |
|
| editor |
|
|
| author
(primary) |
| ARLID |
cav_un_auth*0101091 |
| name1 |
Grim |
| name2 |
Jiří |
| 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*0021087 |
| name1 |
Kittler |
| name2 |
J. |
| country |
GB |
|
| author
|
| 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. |
|
| COSATI |
12B |
| COSATI |
09K |
| cas_special |
| project |
| project_id |
IAA2075703 |
| agency |
GA AV |
| country |
CZ |
| ARLID |
cav_un_auth*0218731 |
|
| project |
| project_id |
VS96063 |
| agency |
MŠMT |
| country |
CZ |
| ARLID |
cav_un_auth*0025066 |
|
| project |
| project_id |
KSK1075601 |
| agency |
GA AV |
| country |
CZ |
| ARLID |
cav_un_auth*0027435 |
|
| research |
AV0Z1075907 |
| abstract
(eng) |
The paper summarizes main features of a new probabilistic approach to neural networks in the framework of statistical pattern recognition. Assuming approximation of class-conditional distributions by finite mixtures we identify formal neurons with the components of finite mixtures and therefore the EM algorithm can be used to optimize the parameters of neurons. In order to prevent the arising information loss we propose a parallel use of the output variables to design the Bayesian classifier. |
| action |
| ARLID |
cav_un_auth*0212643 |
| name |
First International Workshop MCS 2000 |
| place |
Cagliari |
| country |
IT |
| dates |
21.06.2000-23.06.2000 |
|
| RIV |
BB |
| department |
RO |
| permalink |
http://hdl.handle.net/11104/0130422 |
| ID_orig |
UTIA-B 20000048 |
| arlyear |
2000 |
| mrcbU10 |
2000 |
| mrcbU10 |
Berlin Springer |
| mrcbU12 |
3-540-67704-6 |
| mrcbU63 |
Multiple Classifier Systems 157 166 |
| mrcbU67 |
Kittler J. 340 |
| mrcbU67 |
Roli F. 340 |
|