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Classification and Data Analysis in Vector Spaces

  • Chapter
Pattern Recognition

Abstract

Here, as in Chapters 3 and 5, we shall primarily be concerned with methods for making decisions. We shall assume that the primary pattern has already been coded to yield a vector containing numeric descriptors. Such a pattern description is natural in a wide variety of applications, as the following examples show:

  1. 1.

    An autoanalyzer* may be used to define a multielement vector which describes the hormone, protein, salt, and sugar concentrations in human blood.

  2. 2.

    A time-varying signal, such as an EEG or ECG, may be applied to a set of parallel band-pass filters whose outputs are rectified and then integrated. The outputs from the integrators represent the elements of the measurement vector.

  3. 3.

    The color of vegetation, as seen from a satellite, may be used to identify certain crops. A “color” vector might contain three measurements on components from the visible spectrum, as well as ultraviolet or infrared measurements.

A multichannel instrument for performing chemical titrations on a routine basis.

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Authors and Affiliations

  1. Department of Electronics, University of Southampton, Southampton, England

    B. G. Batchelor

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  1. B. G. Batchelor
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Editors and Affiliations

  1. University of Southampton, England

    Bruce G. Batchelor

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© 1978 Plenum Press, New York

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Batchelor, B.G. (1978). Classification and Data Analysis in Vector Spaces. In: Batchelor, B.G. (eds) Pattern Recognition. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-4154-3_4

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  • DOI: https://doi.org/10.1007/978-1-4613-4154-3_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4156-7

  • Online ISBN: 978-1-4613-4154-3

  • eBook Packages: Springer Book Archive

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