Abstract
Numerical pattern classification deals with the problem of assigning feature vectors c to a class Ω K of the available classes Ω = {Ω1, Ω2,..., Ω K }. The features are computed from (noisy) sensor data, like images or speech signals. We postulate that signals can be associated with features which allow the classification, i.e., features of different classes should be different and separated from each other. Features belonging to the same class are thus expected to occupy a compact area of the feature space [Nie90a].
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© 1997 Springer Fachmedien Wiesbaden
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Paulus, D.W.R., Hornegger, J. (1997). Numerical Pattern Classification. In: Pattern Recognition of Images and Speech in C++. Vieweg Advanced Studies in Computer Science. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-663-13991-1_24
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DOI: https://doi.org/10.1007/978-3-663-13991-1_24
Publisher Name: Vieweg+Teubner Verlag, Wiesbaden
Print ISBN: 978-3-528-05558-5
Online ISBN: 978-3-663-13991-1
eBook Packages: Springer Book Archive