Share Email Print
cover

Proceedings Paper

Support vector machines with the correlation kernel for the classification of Raman spectra
Author(s): Alexandros Kyriakides; Evdokia Kastanos; Katerina Hadjigeorgiou; Costas Pitris
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

The range of applications of Raman-based classification has expanded significantly, including applications in bacterial identification. The first stage in the classification of Raman spectra is commonly some form of preprocessing. This pre-processing greatly affects the accuracy of the results and introduces user bias and over-fitting effects. In this paper, we propose the use of Support Vector Machines with a novel correlation kernel. Results, obtained from the analysis of Raman spectra of bacteria, illustrate that the correlation kernel is "self-normalizing" and produces superior classification performance with minimal pre-processing, even on highly-noisy data obtained using inexpensive equipment. In addition, the performance does not degrade when applied to distinct test sets, a key feature of a clinically viable diagnostic application of Raman Spectroscopy.

Paper Details

Date Published: 22 February 2011
PDF: 7 pages
Proc. SPIE 7890, Advanced Biomedical and Clinical Diagnostic Systems IX, 78901B (22 February 2011); doi: 10.1117/12.873308
Show Author Affiliations
Alexandros Kyriakides, Univ. of Cyprus (Cyprus)
KIOS Research Ctr. for Intelligent Systems and Networks (Cyprus)
Evdokia Kastanos, Univ. of Nicosia (Cyprus)
Katerina Hadjigeorgiou, Univ. of Cyprus (Cyprus)
Costas Pitris, Univ. of Cyprus (Cyprus)
KIOS Research Ctr. for Intelligent Systems and Networks (Cyprus)


Published in SPIE Proceedings Vol. 7890:
Advanced Biomedical and Clinical Diagnostic Systems IX
Anita Mahadevan-Jansen; Tuan Vo-Dinh; Warren S. Grundfest, Editor(s)

© SPIE. Terms of Use
Back to Top