8 June 2011 Raman spectra classification with support vector machines and a correlation kernel
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Support Vector Machines have been used successfully for the classification of data in a wide range of applications. A key factor affecting the accuracy of the classification is the choice of kernel. In this paper we propose the use of Support Vector Machines with a correlation kernel. The correlation kernel is an appropriate choice when performing classification of Raman spectra because it reduces the need for pre-processing. Pre-processing can greatly affect the accuracy of the results because it introduces user bias and over-fitting effects. The correlation kernel is "self-normalizing" and produces superior classification performance with minimal pre-processing. Our results show that the performance on highly-noisy data, obtained using inexpensive equipment, is still high even when the classification is applied on a distinct hold-out set of test data. This is an important consideration when developing clinically viable diagnostic applications.
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Alexandros Kyriakides, Alexandros Kyriakides, Evdokia Kastanos, Evdokia Kastanos, Katerina Hadjigeorgiou, Katerina Hadjigeorgiou, Costas Pitris, Costas Pitris, } "Raman spectra classification with support vector machines and a correlation kernel", Proc. SPIE 8087, Clinical and Biomedical Spectroscopy and Imaging II, 808706 (8 June 2011); doi: 10.1117/12.889763; https://doi.org/10.1117/12.889763

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