caa a22 4500 469129182 CHVBK 20180323133159.0 cr unu---uuuuu 170328e19920401xx s 000 0 eng 10.1007/BF00322707 doi (NATIONALLICENCE)springer-10.1007/BF00322707 Comparing the performance of neural networks to well-established methods of multivariate data analysis: the classification of mass spectral data [Elektronische Daten] [H. Lohninger, F. Stancl] Summary: The performance of neural networks in classifying mass spectral data is evaluated and compared to methods of multivariate data analysis and pattern recognition. Back propagation networks are matched with linear discriminant analysis, Kohonen feature maps are compared to the knearest neighbour clustering algorithm. Eight classifiers were trained, in order to discriminate mass spectra of steroids from eight distinct classes of chemical compounds. The results obtained show slightly better performance of Kohonen networks compared to k-nearest neighbour clustering and equal performance of multi-layer perceptrons and discriminant analysis. Springer-Verlag, 1992 Lohninger H. Institute for General Chemistry, Technical University Vienna, Lehargasse 4, A-1060, Vienna, Austria aut Stancl F. Institute for General Chemistry, Technical University Vienna, Lehargasse 4, A-1060, Vienna, Austria aut Fresenius' Journal of Analytical Chemistry Springer-Verlag 344/4-5(1992-04-01), 186-189 0937-0633 344:4-5<186 1992 344 216 https://doi.org/10.1007/BF00322707 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/BF00322707 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lohninger H. Institute for General Chemistry, Technical University Vienna, Lehargasse 4, A-1060, Vienna, Austria aut NATIONALLICENCE 700 1- Stancl F. Institute for General Chemistry, Technical University Vienna, Lehargasse 4, A-1060, Vienna, Austria aut NATIONALLICENCE 773 0- Fresenius' Journal of Analytical Chemistry Springer-Verlag 344/4-5(1992-04-01), 186-189 0937-0633 344:4-5<186 1992 344 216 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer