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Feature construction and parameter setting for Support Vector Machines

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F03%3A00009614" target="_blank" >RIV/00216224:14330/03:00009614 - isvavai.cz</a>

  • Result on the web

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    Feature construction and parameter setting for Support Vector Machines

  • Original language description

    Support Vector Machines (SVM) are a machine learning algorithm that can be used for both classification and regression problems. In this paper, we focus on two problems with SVM. First, we concentrate on the parameter setting of SVM which has great influence on the performance. Then feature construction is discussed. Features can be used to improve results of SVM and to represent structured data in SVM. Mining frequent patterns from structured data is used to construct features.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2003

  • 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 of the 2nd Conference Znalosti 2003

  • ISBN

    80-248-0229-5

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    327-332

  • Publisher name

    VŠB-TUO, Ostrava

  • Place of publication

    Ostrava

  • Event location

    Ostrava

  • Event date

    Jan 1, 2003

  • Type of event by nationality

    CST - Celostátní akce

  • UT code for WoS article