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Classification Based on Multiple-Resolution Data View

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Artificial Neural Networks – ICANN 2010 (ICANN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6354))

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Abstract

We examine efficacy of a classifier based on average of kernel density estimators; each estimator corresponds to a different data “resolution”. Parameters of the estimators are adjusted to minimize the classification error. We propose properties of the data for which our algorithm should yield better results than the basic version of the method. Next, we generate data with postulated properties and conduct numerical experiments. Analysis of the results shows potential advantage of the new algorithm when compared with the baseline classifier.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, Plac Politechniki 1, 00-661, Warsaw, Poland

    Mateusz Kobos & Jacek Mańdziuk

Authors
  1. Mateusz Kobos
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  2. Jacek Mańdziuk
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Editor information

Editors and Affiliations

  1. Department of Informatics, TEI of Thessaloniki, 57400, Sindos, Greece

    Konstantinos Diamantaras

  2. Department of Informatics, Nicolaus Copernicus University, School of Physics, Astronomy, and Informatics, ul. Grudziadzka 5, 87-100, Torun, Poland

    Wlodek Duch

  3. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200, Orestiada Thrace, Greece

    Lazaros S. Iliadis

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Kobos, M., Mańdziuk, J. (2010). Classification Based on Multiple-Resolution Data View. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_16

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  • DOI: https://doi.org/10.1007/978-3-642-15825-4_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15824-7

  • Online ISBN: 978-3-642-15825-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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