Overview: Classification

  • Christian Demant
  • Carsten GarnicaEmail author
  • Bernd Streicher-Abel


This chapter will give a brief general introduction into the field of classification and an overview of some important types of classifiers. Classification is a research area in its own right, which over the last 40 years has combined results from disciplines as different as biology, psychology, mathematics, and computer science. Covering such an extensive and varied scientific field with even a minimal claim to completeness is naturally beyond the scope of this text, as is treating all commonly used types of classifiers in mathematical detail. Yet we will at least mention the principal types to give the reader some orientation in this important area of pattern recognition tasks. We will therefore discuss the multilayer perceptron neural network used in the pattern recognition examples of Chap. 5 in depth.


Pattern Recognition Example Instance-based Classifier Prototype Pattern Border Problems Classical Polynomial 
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.


  1. Bishop CM (1995) Neural networks and pattern recognition. Oxford University Press, OxfordGoogle Scholar
  2. Brause R (1991) Neuronale Netze. Teubner, StuttgartCrossRefzbMATHGoogle Scholar
  3. Bronstein IN, Semendjajew KA, Musiol G, Mühlig H (2005) Taschenbuch der Mathematik, 6th edn. Harri Deutsch, Frankfurt a. MGoogle Scholar
  4. Kohonen T (1989) Self-organization and associative memory. Springer, BerlinCrossRefGoogle Scholar
  5. Pao YH (1989) Adaptive pattern recognition and neural networks. Addison-Wesley, ReadingGoogle Scholar
  6. Reilly DL, Cooper LN, Elbaum C (1982) A neural model for category learning. Biol Cybern 45:35–41CrossRefGoogle Scholar
  7. Schürmann J (1977) Polynomklassifikatoren für die Zeichenerkennung: Ansatz, Adaption. Anwendungen. R, MünchenzbMATHGoogle Scholar
  8. Smith M (1993) Neural networks for statistical modeling. Van Nostrand Reinhold, New YorkzbMATHGoogle Scholar
  9. Zell A (1994) Simulation neuronaler Netze. Addison-Wesley, BonnzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Christian Demant
    • 1
  • Carsten Garnica
    • 1
    Email author
  • Bernd Streicher-Abel
    • 1
  1. 1.NeuroCheck GmbHRemseckGermany

Personalised recommendations