bibtype J - Journal Article
ARLID 0410828
utime 20240103182242.1
mtime 20060210235959.9
title (primary) (eng) Multiple classifier fusion in probabilistic neural networks
specification
page_count 13 s.
serial
ARLID cav_un_epca*0254665
ISSN 1433-7541
title Pattern Analysis and Applications
volume_id 5
volume 7 (2002)
page_num 221-233
keyword EM algorithm
keyword information preserving transform
keyword multiple classifier fusion
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 GA402/01/0981
agency GA ČR
ARLID cav_un_auth*0008962
research CEZ:AV0Z1075907
abstract (eng) The main motivation of the present paper is to design a statistically well justified and biologically compatible neural network model and to suggest a theoretical interpretation of the high parallelism of biological neural networks. We consider a probabilistic approach to neural networks in the framework of statistical pattern recognition. The complete method based on EM algorithm has been applied to recognize unconstrained handwritten numerals from the database of the Concordia University Montreal.
RIV BB
department RO
permalink http://hdl.handle.net/11104/0130915
ID_orig UTIA-B 20020042
arlyear 2002
mrcbU63 cav_un_epca*0254665 Pattern Analysis and Applications 1433-7541 1433-755X Roč. 5 č. 7 2002 221 233