On Robust Training of Regression Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00525292" target="_blank" >RIV/67985807:_____/20:00525292 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-47756-1_20" target="_blank" >http://dx.doi.org/10.1007/978-3-030-47756-1_20</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-47756-1_20" target="_blank" >10.1007/978-3-030-47756-1_20</a>
Alternative languages
Result language
angličtina
Original language name
On Robust Training of Regression Neural Networks
Original language description
Estimation, prediction or smoothing of curves represents a fundamental task of functional data analysis. Nonlinear regression methods allow to search for the best-fit curves explaining the dependence of a response variable on available independent variables. Neural networks, commonly used for the task of nonlinear regression, are however highly vulnerable to the presence of outlying measurements in the data. New robust versions of common types of neural networks, namely multilayer perceptrons and radial basis function networks, are proposed here based on nonlinear regression quantiles or highly robust loss functions. Three datasets are analyzed to illustrate the performance of the novel robust approaches, which turn out to outperform standard neural networks or other competing regression tools over contaminated data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Functional and High-Dimensional Statistics and Related Fields
ISBN
978-3-030-47755-4
ISSN
1431-1968
e-ISSN
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Number of pages
8
Pages from-to
145-152
Publisher name
Springer
Place of publication
Cham
Event location
Online
Event date
Jun 23, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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