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International Workshop on Machine Learning and Data Mining in Pattern Recognition

MLDM 2003: Machine Learning and Data Mining in Pattern Recognition pp 65–72Cite as

On the Size of a Classification Tree

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2734))

Abstract

We discuss some estimates for the misclassification rate of a classification tree in terms of the size of the learning set, following some ideas introduced in [3]. We develop some mathematical ideas of [3], extending the analysis to the case with an arbitrary finite number of classes.

Partly supported by a grant of the University of Rome “La Sapienza”

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References

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Author information

Authors and Affiliations

  1. Facoltà di Sociologia, Università di Roma “La Sapienza”, via Salaria 113, I00198, Rome, Italy

    Angela Scaringella

Authors
  1. Angela Scaringella
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Arndtstr. 4, 04275, Leipzig, Germany

    Petra Perner

  2. Center for Automation Research, University of Maryland, College Park, Maryland, 20742-3275, USA

    Azriel Rosenfeld

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© 2003 Springer-Verlag Berlin Heidelberg

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Scaringella, A. (2003). On the Size of a Classification Tree. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_6

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  • DOI: https://doi.org/10.1007/3-540-45065-3_6

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40504-7

  • Online ISBN: 978-3-540-45065-8

  • eBook Packages: Springer Book Archive

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