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Classifier Combination as a Tomographic Process

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Multiple Classifier Systems (MCS 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2096))

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Abstract

A mathematical analogy between the process of multiple expert fusion and the tomographic reconstruction of Radon integral data is outlined for the specific instance of the combination of classifiers containing discrete data sets. Within this metaphor all conventional methods of classifier combination come, to a greater or lesser degree, to resemble the unfiltered back-projection of the constituent classifiers’ probability density functions: an implicit attempt to reconstruct the PDF of the composite pattern space. In these probabilistic terms, the combination of classifiers with identical feature-sets correspondingly constitutes an attempt at morphological manipulation of the composite pattern-space PDF. A consideration of the separate benefits of combination along these dualistic lines eventually leads to an optimal strategy for classifier combination under arbitrary conditions.

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References

  1. D. Windridge, J. Kittler, “Combined Classifier Optimisation via Feature Selection”, Proceedings “Advances in Pattern Recognition”, Joint IAPR International Workshops SSPR 2000 and SPR 2000 Alicante, Spain, August 30–September 1, 2000, Lecture Notes in Computer Science. VOL. 1876

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© 2001 Springer-Verlag Berlin Heidelberg

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Windridge, D., Kittler, J. (2001). Classifier Combination as a Tomographic Process. In: Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48219-9_25

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  • DOI: https://doi.org/10.1007/3-540-48219-9_25

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42284-6

  • Online ISBN: 978-3-540-48219-2

  • eBook Packages: Springer Book Archive

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