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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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • 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

  • 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