bibtype	K - Conference Paper (Czech conference)
ARLID	0481488
utime	20240103214946.0
mtime	20171116235959.9
WOS	000427151400050
title (primary) (eng)	Avoiding overfitting of models: an application to research data on the Internet videos
specification	page_count 6 s. media_type E
serial	ARLID cav_un_epca*0477966 ISBN 978-80-7435-678-0 title Proceedings of the 35th International Conference Mathematical Methods in Economics (MME 2017) page_num 289-294 publisher place Hradec Králové name University of Hradec Králové year 2017
keyword	data-based learning
keyword	probabilistic models
keyword	information theory
keyword	MDL principle
keyword	lossless encoding
author (primary)	ARLID cav_un_auth*0101118 full_dept (cz) Matematická teorie rozhodování full_dept (eng) Department of Decision Making Theory department (cz) MTR department (eng) MTR full_dept Department of Decision Making Theory share 75 name1 Jiroušek name2 Radim institution UTIA-B garant K fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author	ARLID cav_un_auth*0353476 share 25 name1 Krejčová name2 I. country CZ
source	url http://library.utia.cas.cz/separaty/2017/MTR/jirousek-0481488.pdf
cas_special	project ARLID cav_un_auth*0353428 project_id GA15-00215S agency GA ČR country CZ abstract (eng) The problem of overfitting is studied from the perspective of information theory. In this context, data-based model learning can be viewed as a transformation process, a process transforming the information contained in data into the information represented by a model. The overfitting of a model often occurs when one considers an unnecessarily complex model, which usually means that the considered model contains more information than the original data. Thus, using one of the basic laws of information theory saying that any transformation cannot increase the amount of information, we get the basic restriction laid on models constructed from data: A model is acceptable if it does not contain more information than the input data file. action ARLID cav_un_auth*0346896 name MME 2017. International Conference Mathematical Methods in Economics /35./ dates 20170913 mrcbC20-s 20170915 place Hradec Králové country CZ RIV AH FORD0 50000 FORD1 50200 FORD2 50202 reportyear 2018 num_of_auth 2 presentation_type PR inst_support RVO:67985556 permalink http://hdl.handle.net/11104/0277045 cooperation ARLID cav_un_auth*0353468 name Výsoká škola ekonomická, Fakulta managementu institution FM VŠE country CZ confidential S mrcbC86 3+4 Proceedings Paper Economics|Operations Research Management Science|Mathematics Interdisciplinary Applications|Social Sciences Mathematical Methods mrcbC86 3+4 Proceedings Paper Economics|Operations Research Management Science|Mathematics Interdisciplinary Applications|Social Sciences Mathematical Methods mrcbC86 3+4 Proceedings Paper Economics|Operations Research Management Science|Mathematics Interdisciplinary Applications|Social Sciences Mathematical Methods arlyear 2017 mrcbU34 000427151400050 WOS mrcbU63 cav_un_epca*0477966 Proceedings of the 35th International Conference Mathematical Methods in Economics (MME 2017) 978-80-7435-678-0 289 294 Hradec Králové University of Hradec Králové 2017