Data Splitting

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F10%3A10080310" target="_blank" >RIV/00216208:11320/10:10080310 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Data Splitting
Original language description
In machine learning, one of the main requirements is to build computational models with a high ability to generalize well the extracted knowledge. When training e.g. artificial neural networks, poor generalization is often characterized by over-training.A common method to avoid over-training is the hold-out cross-validation. The basic problem of this method represents, however, appropriate data splitting. In most of the applications, simple random sampling is used. Nevertheless, there are several sophisticated statistical sampling methods suitable for various types of datasets. This paper provides a survey of existing sampling methods applicable to the data splitting problem. Supporting experiments evaluating the benefits of the selected data splitting techniques involve artificial neural networks of the back-propagation type.
Czech name
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Czech description
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Project
<a href="/en/project/GD201%2F09%2FH057" target="_blank" >GD201/09/H057: Res Informatica</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
WDS'10 Proceedings of Contributed Papers: Part I - Mathematics and Computer Sciences
ISBN
978-80-7378-139-2
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
Matfyzpress
Place of publication
Praha
Event location
Praha
Event date
Jun 1, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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