bibtype C - Conference Paper (international conference)
ARLID 0389404
utime 20240103202212.2
mtime 20130221235959.9
title (primary) (eng) Identification of Corporate Competitiveness Factors – Comparing Different Approaches
specification
page_count 9 s.
media_type P
serial
ARLID cav_un_epca*0389551
ISBN 978-1-909507-00-5
ISSN 2049-6818
title Proceedings of the International Conference on Management, Leadership and Governance 2013
page_num 259-267
publisher
place Reading
name Academic Conferences and Publishing International Limited
year 2013
keyword factors of corporate competitiveness
keyword corporate financial performance
keyword empirical research
keyword non-linear regression
keyword feature selection
keyword statistical pattern recognition
author (primary)
ARLID cav_un_auth*0021092
name1 Pudil
name2 P.
country CZ
author
ARLID cav_un_auth*0289238
name1 Blažek
name2 L.
country CZ
author
ARLID cav_un_auth*0289239
name1 Částek
name2 O.
country CZ
author
ARLID cav_un_auth*0289363
name1 Somol
name2 P.
country CZ
author
ARLID cav_un_auth*0101091
name1 Grim
name2 Jiří
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
url http://library.utia.cas.cz/separaty/2013/RO/grim-identification of corporate competitiveness factors - comparing different approaches.pdf
cas_special
project
project_id GAP403/12/1557
agency GA ČR
country CZ
ARLID cav_un_auth*0308953
abstract (eng) The methodology and current results of identifying factors of corporate competitiveness in the Czech Republic are discussed. The task is to investigate what is the mutual dependency between the corporate competitiveness (characterized here by the corporate financial performance (hereinafter called ‘CFP) and selected characteristics describing these companies. Such characteristics can be regarded as the factors of competitiveness. The task of determining these factors has to be solved in multidimensional space. Therefore, the feature selection methodology from statistical pattern recognition, selecting a group of the most informative characteristics appears to be a suitable and promising approach. As opposed to our recent paper based on the classification approach, an alternative approach based on non-linear statistical regression is presented here. The paper presents a brief introduction to both the approaches and the results achieved when using them.
action
ARLID cav_un_auth*0289240
name International Conference on Management, Leadership and Governance
place Bangkok
dates 07.02.2013-08.02.2013
country TH
reportyear 2013
RIV AH
num_of_auth 5
presentation_type PR
inst_support RVO:67985556
permalink http://hdl.handle.net/11104/0218411
arlyear 2013
mrcbU63 cav_un_epca*0389551 Proceedings of the International Conference on Management, Leadership and Governance 2013 978-1-909507-00-5 2049-6818 259 267 Reading Academic Conferences and Publishing International Limited 2013