Machine learning approches and pattern recognition for spectral data.
Thomas Villmann, Erzsébet Merényi, Udo Seiffert · The European Symposium on Artificial Neural Networks · 2008
The adaptive and automated analysis of spectral data plays an important role in many areas of research such as physics, astronomy and geophysics, chemistry, bioinformatics, biochemistry, engineering, and others. The amount of data may range from several billion samples in geophysics to only a few in medical applications. Further, a vectorial rep- resentation of spectra typically leads to huge-dimensional problems. This scenario gives the background for particular requirements of respective machine learning approaches which will be the focus of this overview.