Unsupervised Learning Techniques

  • Oliver Nelles


In so-called unsupervised learning the desired model output y is not known or is assumed to be not known. The goal of unsupervised learning methods is to process or extract information with the knowledge about the input data \(\left\{ {\underset{\raise0.3em\hbox{$\smash{\scriptscriptstyle-}$}}{u} \left( i \right)} \right\},i = 1, \cdots ,N\) , only. In all problems addressed in this book the desired output is known. However, unsupervised learning techniques can be very interesting and helpful for data preprocessing; see Fig. 6.1. Preprocessing transforms the data into another form, which hopefully can be better processed by the subsequent model. In this context, it is important to keep in mind that the desired output is actually available, and there may exist some efficient way to include this knowledge even into the preprocessing phase.


Cluster Center Cluster Technique Neighborhood Function Adaptive Resonance Theory Winner Neuron 
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 2001

Authors and Affiliations

  • Oliver Nelles
    • 1
  1. 1.UC Berkeley / TU DarmstadtKronbergGermany

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