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Rubinshtein evgenia (3 risultati)

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  • Lingua: Inglese

    Editore: VDM Verlag Dr. Müller, 2009

    3639133994 / 9783639133998

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    Da: preigu, Osnabrück, Germaniapreigu

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    Condizione: Nuovo

    EUR 51,10

    EUR 70,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 5 disponibili

    Taschenbuch. Condizione: Neu. Optimal Linear Representations of Images Under Diverse Criteria | Geometric Tools for Finding Linear Projections | Evgenia Rubinshtein | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639133998 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

  • Lingua: Inglese

    Editore: VDM Verlag Dr. Müller, 2009

    3639133994 / 9783639133998

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    • Print on Demand

    Da: moluna, Greven, Germaniamoluna

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    EUR 46,32

    EUR 48,99 spedizione 
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    Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Rubinshtein EvgeniaEvgenia Rubinshtein, Ph.D: Studied Statistics at Florida State nUniversity. Associate Professor at Vladivostok State University nof Economics and Service and at Far Eastern State Technical nUniversity, Vladivostok..…

  • Lingua: Inglese

    Editore: VDM Verlag Dr. Müller, 2009

    3639133994 / 9783639133998

    • Brossura
    • Print on Demand

    Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH

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    Condizione: Nuovo

    EUR 66,55

    EUR 35,00 spedizione 
    Spedito da Germania a U.S.A.

    Quantità: 2 disponibili

    Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Image analysis often requires dimension reduction before statistical analysis, in order to apply sophisticated procedures. Motivated by eventual applications, a variety of criteria have been proposed: reconstruction error, class separation, non-Gaussianity using kurtosis, sparseness, mutual information, recognition of objects, and their combinations. Although some criteria have analytical solutions, the remaining ones require numerical approaches. We present geometric tools for finding linear projections that optimize a given criterion for a given data set. The main idea is to formulate a problem of optimization on a Grassmann or a Stiefel manifold, and to use differential geometry of the underlying space to construct optimization algorithms. Purely deterministic updates lead to local solutions, and addition of random components allows for stochastic gradient searches that eventually lead to global solutions. We demonstrate these results using several image datasets, including natural images and facial images. This book should be useful for professionals, researches and graduate students in Image Analysis field. …