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Laszlo Gyorfi – författare

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4 produkter

  1. Harro Walk, Adam Krzyzak, Michael Kohler, Laszlo Gyorfi - Distribution-Free Theory of Nonparametric Regression, E-bok. Tillgänglighet: Lägg i varukorg

    Distribution-Free Theory of Nonparametric Regression

    Av Harro Walk, Adam Krzyzak m. fl.

    E-bok, 2006

    4718 kr

    Lägg i varukorg

    The regression estimation problem has a long history. Already in 1632 Galileo Galilei used a procedure which can be interpreted as ?tting a linear relationship to contaminated observed data. Such ?tting of a line through a cloud of points is the classical linear regression problem. A solution of …

  2. Gabor Lugosi, Laszlo Gyorfi, Luc Devroye - Probabilistic Theory of Pattern Recognition, E-bok. Tillgänglighet: Lägg i varukorg

    Probabilistic Theory of Pattern Recognition

    Av Gabor Lugosi, Laszlo Gyorfi m. fl.

    E-bok, 2013

    1434 kr

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    Pattern recognition presents one of the most significant challenges for scientists and engineers, and many different approaches have been proposed. The aim of this book is to provide a self-contained account of probabilistic analysis of these approaches. The book includes a discussion of distance …

  3. Thomas Zeugmann, Gyorgy Turan, Laszlo Gyorfi, Yoav Freund - Algorithmic Learning Theory, E-bok. Tillgänglighet: Lägg i varukorg

    Algorithmic Learning Theory

    19th International Conference, ALT 2008, Budapest, Hungary, October 13-16, 2008, Proceedings

    Av Thomas Zeugmann, Gyorgy Turan m. fl.

    E-bok, 2008

    734 kr

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    This volume contains papers presented at the 19th International Conference on Algorithmic Learning Theory (ALT 2008), which was held in Budapest, Hungary during October 13–16, 2008. The conference was co-located with the 11th - ternational Conference on Discovery Science (DS 2008). The technical …

  4. Laszlo Gyorfi - Principles of Nonparametric Learning, E-bok. Tillgänglighet: Lägg i varukorg

    Principles of Nonparametric Learning

    Av Laszlo Gyorfi

    E-bok, 2014

    1948 kr

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    The book provides systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density …