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Rough-Neural Computing pp 139–156Cite as

Adaptive Aspects of Combining Approximation Spaces

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Part of the book series: Cognitive Technologies ((COGTECH))

Summary

This chapter addresses issues concerning a problem of constructing an optimal classification algorithm. The notion of a parameterized approximation space is used to model the process of classifier construction. The process can be viewed as hierarchical searching for optimal information granulation to fit a concept described by empirical data. The problem of combining several parameterized information granules (given by classification algorithms) to obtain a global data description is described. Some solutions based on adaptive methods are presented.

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Author information

Authors and Affiliations

  1. Polish-Japanese Institute of Information Technology, Koszykowa 86, 02-008, Warsaw, Poland

    Jakub Wróblewski

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  1. Jakub Wróblewski
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Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Machine Intelligence Unit, 203 Barrackpore Trunk Road, Calcutta, 700 035, India

    Sankar K. Pal

  2. Department of Mathematics and Computer Science, University of Warmia and Mazury, Zolnierska 14, Olsztyn, Poland

    Lech Polkowski

  3. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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© 2004 Springer-Verlag Berlin Heidelberg

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Wróblewski, J. (2004). Adaptive Aspects of Combining Approximation Spaces. In: Pal, S.K., Polkowski, L., Skowron, A. (eds) Rough-Neural Computing. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18859-6_6

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  • DOI: https://doi.org/10.1007/978-3-642-18859-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-62328-8

  • Online ISBN: 978-3-642-18859-6

  • eBook Packages: Springer Book Archive

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