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Feedforward Neural Network Models for Spatial Data Classification and Rule Learning

In: Recent Developments in Spatial Analysis

Author

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  • Yee Leung

    (The Chinese University of Hong Kong)

Abstract

Spatial data classification has long been a major field of research in geographical analysis. Regardless of whether we are classifying statistical data into socioeconomic patterns or remotely sensed data into land covers, our classification task is to group high dimensional data into separate clusters which represent distinguishable spatial features or patterns.

Suggested Citation

  • Yee Leung, 1997. "Feedforward Neural Network Models for Spatial Data Classification and Rule Learning," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 17, pages 336-359, Springer.
    • Handle: RePEc:spr:adspcp:978-3-662-03499-6_17
    DOI: 10.1007/978-3-662-03499-6_17
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    Cited by:

    1. M M Fischer, 1998. "Computational Neural Networks: A New Paradigm for Spatial Analysis," Environment and Planning A, , vol. 30(10), pages 1873-1891, October.

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