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Comparing Non-Linear Regression Methods on Black-Box Optimization Benchmarks

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F15%3A00447915" target="_blank" >RIV/67985807:_____/15:00447915 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Comparing Non-Linear Regression Methods on Black-Box Optimization Benchmarks

  • Original language description

    The paper compares several non-linear regression methods on synthetic data sets generated using standard benchmarks for continuous black-box optimization. For that comparison, we have chosen regression methods that have been used as surrogate models in such optimization: radial basis function networks, Gaussian processes, and random forests. Because the purpose of black-box optimization is frequently some kind of design of experiments, and because a role similar to surrogate models is in the traditionaldesign of experiments played by response surface models, we also include standard response surface models, i.e., polynomial regression. The methods are evaluated based on their mean-squared error and on the Kendall?s rank correlation coefficient betweenthe ordering of function values according to the model and according to the function used to generate the data.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2015

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    Proceedings ITAT 2015: Information Technologies - Applications and Theory

  • ISBN

    978-1-5151-2065-0

  • ISSN

    1613-0073

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    135-142

  • Publisher name

    Technical University & CreateSpace Independent Publishing Platform

  • Place of publication

    Aachen & Charleston

  • Event location

    Slovenský Raj

  • Event date

    Sep 17, 2015

  • Type of event by nationality

    EUR - Evropská akce

  • UT code for WoS article