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Unsupervised Learning Techniques

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Nonlinear System Identification

Abstract

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.

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© 2001 Springer-Verlag Berlin Heidelberg

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Nelles, O. (2001). Unsupervised Learning Techniques. In: Nonlinear System Identification. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-04323-3_6

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  • DOI: https://doi.org/10.1007/978-3-662-04323-3_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-08674-8

  • Online ISBN: 978-3-662-04323-3

  • eBook Packages: Springer Book Archive

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