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Comparison Between Mini-models Based on Multidimensional Polytopes and k-nearest Neighbor Method: Case Study of 4D and 5D Problems

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Soft Computing in Computer and Information Science

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 342))

Abstract

This paper presents the comparison between mini-models method based on multidimensional polytopes and k-nearest neighbor method. Both algorithms are similar, and both methods use samples only from the local neighborhood of the query point. The mini-models method can on the defined local area use any approximation algorithm to compute the model answer. The paper describes the learning technique of mini-models and presents the results of experiments that compare the effectiveness of two examined algorithms.

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Authors and Affiliations

  1. West Pomeranian University of Technology, Faculty of Computer Science and Information Technology, Żołnierska 49, 71-210, Szczecin, Poland

    Marcin Pietrzykowski

Authors
  1. Marcin Pietrzykowski
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Correspondence to Marcin Pietrzykowski .

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Editors and Affiliations

  1. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland

    Antoni Wiliński

  2. West Pomeranian University of Technology, Szczecin, Poland

    Imed El Fray

  3. Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland

    Jerzy PejaÅ›

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Pietrzykowski, M. (2015). Comparison Between Mini-models Based on Multidimensional Polytopes and k-nearest Neighbor Method: Case Study of 4D and 5D Problems. In: Wiliński, A., Fray, I., Pejaś, J. (eds) Soft Computing in Computer and Information Science. Advances in Intelligent Systems and Computing, vol 342. Springer, Cham. https://doi.org/10.1007/978-3-319-15147-2_10

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  • DOI: https://doi.org/10.1007/978-3-319-15147-2_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15146-5

  • Online ISBN: 978-3-319-15147-2

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