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Selection of Most Informative Variables in Statistical Pattern Recognition

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    SYSNO ASEP0083328
    Document TypeV - Research Report
    R&D Document TypeThe record was not marked in the RIV
    TitleSelection of Most Informative Variables in Statistical Pattern Recognition
    TitleVýběr nejinformativnějších proměnných ve statistickém rozpoznávání
    Author(s) Pudil, Pavel (UTIA-B) RID
    Somol, Petr (UTIA-B) RID
    Haindl, Michal (UTIA-B) RID, ORCID
    Issue dataPlzeň: UWB, 2007
    SeriesMATEO -The European Network of Mechatronics Centres and Industrial Controllers
    Number of pages87 s.
    Languageeng - English
    CountryCZ - Czech Republic
    Keywordsfeture selection
    Subject RIVBD - Theory of Information
    R&D ProjectsMAT-12-C4 GA MMR - Ministry for Regional Development (MMR)
    1M0572 GA MŠk - Ministry of Education, Youth and Sports (MEYS)
    2C06019 GA MŠk - Ministry of Education, Youth and Sports (MEYS)
    CEZAV0Z10750506 - UTIA-B (2005-2011)
    AnnotationThe research report gives an overview of feature selection techniques in statistical pattern recognition with particular emphasis to methods developed by the researchers participating in MATEO Centre of Mechatronics project. Besides discussing the advances in methodology it attempts to put them into a taxonomical framework. The methods discussed include the latest variants of the optimal algorithms, enhanced sub-optimal techniques and the simultaneous semi-parametric probability density function modelling and feature space selection method. Some related issues are illustrated on real data by means of the Feature Selection Toolbox software.
    WorkplaceInstitute of Information Theory and Automation
    ContactMarkéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201.
    Year of Publishing2008
Number of the records: 1