Pattern Recognition

  • Teuvo Kohonen
Part of the Springer Series in Information Sciences book series (SSINF, volume 8)


Pattern recognition (PR) may be regarded as a special case of associative mappings, namely, as a process in which classes of patterns are directly mapped on a set of discrete elements. Many pattern recognition methods are related to learning schemes; however, the problem is then largely that of mathematical statistics and does not necessarily presuppose physical realizability. On the other hand, as most of the applications of pattern recognition have quickly been pushed to serve practical purposes such as remote sensing or biomedical imaging, the theoretical foundations have remained rather heterogeneous. It is possible to find many excellent textbooks in specialized areas such as image analysis and even statistical methods of pattern recognition, whereas good reviews of the complete field are scarce. An introduction to the general problematics may be obtained from, e.g., the book of Young and Calvert [7.1], and contemporary structural methods of pattern analysis are well presented by Pavlidis [7.2]. On the other hand, to concentrate on one of the special problems, it is advisable to pick up, e.g., one of the books of which a listing is given in the Bibliography.


Discriminant Function Minimal Span Tree Subspace Method Reference Vector Voronoi Tessellation 
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 1989

Authors and Affiliations

  • Teuvo Kohonen
    • 1
  1. 1.Laboratory of Computer and Information SciencesHelsinki University of TechnologyEspoo 15Finland

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