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.
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© 2012 Springer-Verlag Berlin Heidelberg
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Sartorius, G. (2012). Multivariate Adaptive Embedding, MAE-Process. In: Unger, H., Kyamaky, K., Kacprzyk, J. (eds) Autonomous Systems: Developments and Trends. Studies in Computational Intelligence, vol 391. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24806-1_9
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DOI: https://doi.org/10.1007/978-3-642-24806-1_9
Publisher Name: Springer, Berlin, Heidelberg
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