Abstract
Recently a number of successful algorithms to select/extract discriminative spectral regions was introduced. These methods may be more beneficial than the standard feature selection/extraction methods for spectral classification. In this paper, on the example of autofluorescence spectra measured in the oral cavity, we intend to get deeper understanding what might be the best way to select informative spectral regions and what factors may influence the success of this approach.
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© 2004 Springer-Verlag Berlin Heidelberg
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Skurichina, M. et al. (2004). Selection/Extraction of Spectral Regions for Autofluorescence Spectra Measured in the Oral Cavity. In: Fred, A., Caelli, T.M., Duin, R.P.W., Campilho, A.C., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2004. Lecture Notes in Computer Science, vol 3138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-27868-9_121
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DOI: https://doi.org/10.1007/978-3-540-27868-9_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22570-6
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