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Multivariate Adaptive Embedding, MAE-Process

  • Gerhard SartoriusEmail author
Part of the Studies in Computational Intelligence book series (SCI, volume 391)

Abstract

The multivariate adaptive embedding (MAE-Process) provides an adaptive System which creates artificial neural network in the form of an appropriate model of the training-data set by using a globally optimal optimisation and acts in most cases without iterations and parameter settings. There is basically no change to input data and the training-data set is prepared by a special fitting method in order to make it treatable for spectral methods. In the working phase, new input data can be processed multivariatly by the system with good generalising properties. In combination with a Wavelet transformation (WT) for noise- and data reduction, the System performs fast and efficiently in classifying parameterised curves, such as Raman spectra.

Keywords

Machine learning Multi-class classification Cluster identification 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Lehrgebiet InformationstechnikFernuniversität HagenHagenGermany

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