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New Specifics for a Hierarchial Estimator Meta-algorithm

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

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Abstract

Hierarchical Estimator is a meta-algorithm presented in [1] concerned with learning a nonlinear relation between two vector variables from training data, which is one of the core tasks of machine learning, primarily for the purpose of prediction. It arranges many simple function approximators into a tree-like structure in order to achieve a solution with a low error.

This paper presents a new version of specifics for that meta-algorithm – a so called training set division and a competence function creation method. The included experimental results show improvement over the methods described in [1]. A short recollection of Hierarchical Estimator is also included.

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Author information

Authors and Affiliations

  1. Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University, Krakow, Poland

    Stanisław Brodowski

  2. Institute of Computer Science, Faculty of Mathematics and Computer Science, Jagiellonian University, Krakow, Poland

    Andrzej Bielecki

Authors
  1. Stanisław Brodowski
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  2. Andrzej Bielecki
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Brodowski, S., Bielecki, A. (2012). New Specifics for a Hierarchial Estimator Meta-algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_3

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  • DOI: https://doi.org/10.1007/978-3-642-29350-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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