Statistical pattern recognition

  • Ludmila I. Kuncheva
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 49)


Pattern recognition problems emerge constantly in our everyday life. The ring of the telephone triggers an on-line pattern recognition problem: who might this be? Hearing the voice on the line we are almost always able to tell who this is, no matter that the line might be noisy, or the person at the other end might have a cold. Humans easily identify faces, speakers, smells — tasks that are still a challenge for a computer. When we are able to instruct the computer how to label the objects into the prespecified groups, the problem becomes routine. Pattern recognition is about those problems that are still not algorithmically clear-cut. Examples of pattern recognition problems are: classification of crops and soil types from remote-sensing images; detection of clustered microcalcifications on mammograms; optical character recognition (OCR); classification of airmass for predicting a thunderstorm flood; discrimination between stars and galaxies in sky images; etc.


Classification Accuracy Discriminant Function Optical Character Recognition Learning Vector Quantization Linear Discriminant Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ludmila I. Kuncheva
    • 1
  1. 1.School of InformaticsUniversity of WalesBangor GwyneddUK

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