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USING TRANSFORMATION KNOWLEDGE FOR THE CLASSIFICATION OF RAMAN SPECTRA OF BIOLOGICAL SAMPLES
 

Summary: USING TRANSFORMATION KNOWLEDGE FOR THE CLASSIFICATION OF
RAMAN SPECTRA OF BIOLOGICAL SAMPLES
Klaus.-D. Peschke, Bernard Haasdonk,
Olaf Ronneberger and Hans Burkhardt
Lehrstuhl f¨ur Mustererkennung und Bildverarbeitung,
Institut f¨ur Informatik, Albert-Ludwigs-Universit¨at Freiburg,
Georges-K¨ohler-Allee 52, D-79110 Freiburg, Germany
email: peschke@informatik.uni-freiburg.de
Petra R¨osch, Michaela Harz and J¨urgen Popp
Institut f¨ur Physikalische Chemie
Friedrich-Schiller-Universit¨at Jena
Helmholtzweg 4, D-07743 Jena, Germany
ABSTRACT
For the classification of biological samples based on Ra-
man spectra, a robust classifier is necessary. This require-
ment is met by using Support Vector Machines (SVMs)
enhanced by incorporating a-priori knowledge about pat-
tern variations. In the described approach transformation
knowledge is included directly into the classification pro-
cess by using regularized tangent distance kernels. This

  

Source: Albert-Ludwigs-Universität Freiburg, Institut für Informatik,, Lehrstuhls für Mustererkennung und Bildverarbeitung
Haasdonk, Bernard - Institut für Numerische und Angewandte Mathematik, Universität Münster

 

Collections: Computer Technologies and Information Sciences; Mathematics