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Learning with Generalization Capability by Kernel Methods of Bounded Complexity

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F05%3A00405215" target="_blank" >RIV/67985807:_____/05:00405215 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Learning with Generalization Capability by Kernel Methods of Bounded Complexity

  • Original language description

    Learning from data with generalization capability is studied in the framework of minimization of regularized empirical error functionals over nested families of hypothesis sets with increasing model complexity.

  • Czech name

    Učení se schopností generalizace pomocí jádrových metod omezené složitosti

  • Czech description

    Učení na základě dat se schopností generalizace je studováno v rámci minimalizace regularizované empirické chyby na hypotetických množinách s rostoucí modelovou složitostí.

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    BA - General mathematics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/1ET100300419" target="_blank" >1ET100300419: Intelligent Models, Algorithms, Methods and Tools for the Semantic Web (realization)</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2005

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Journal of Complexity

  • ISSN

    0885-064X

  • e-ISSN

  • Volume of the periodical

    21

  • Issue of the periodical within the volume

    -

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    18

  • Pages from-to

    350-367

  • UT code for WoS article

  • EID of the result in the Scopus database