Avoiding overfitting of models: an application to research data on the Internet videos

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00481488" target="_blank" >RIV/67985556:_____/17:00481488 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Avoiding overfitting of models: an application to research data on the Internet videos
Original language description
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.
Czech name
—
Czech description
—

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 35th International Conference Mathematical Methods in Economics (MME 2017)
ISBN
978-80-7435-678-0
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
289-294
Publisher name
University of Hradec Králové
Place of publication
Hradec Králové
Event location
Hradec Králové
Event date
Sep 13, 2017
Type of event by nationality
EUR - Evropská akce
UT code for WoS article
—

Similar results(10)