ISSN:
0886-9383
Schlagwort(e):
Non-parametric method
;
Pattern recognition
;
Classification
;
Chemistry
;
Analytical Chemistry and Spectroscopy
Quelle:
Wiley InterScience Backfile Collection 1832-2000
Thema:
Chemie und Pharmazie
Notizen:
A non-parametric method for supervised pattern recognition is presented. The method is of the class modelling type, meaning that a classification rule is developed for each class, using the dissimilarities between the objects of the class. The dissimilarities between the objects within a class are related to the distances between all pairs of training objects. As distance metric, a measure is proposed that takes the correlation between the interval-scale variables into account, and that moreover can be used for mixed types of variables. The classification rule is based on the construction of a boundary in the measurement space. For the determination of the class boundary, several strategies are proposed and compared.The performance of the technique is evaluated on the basis of several data sets. Comparison with the class modelling technique UNEQ shows its usefulness for practical application.
Zusätzliches Material:
4 Ill.
Materialart:
Digitale Medien
URL:
http://dx.doi.org/10.1002/cem.1180030206
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