Pattern recognition for the classification of Raman spectroscopy signals

Guo Ping; Lu Hanqing; Du Weimin

Volume 24 Issue 6

Jun. 2002

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Guo Ping, Lu Hanqing, Du Weimin. Pattern recognition for the classification of Raman spectroscopy signals[J]. Journal of Electronics & Information Technology, 2002, 24(6): 789-793.

Citation:

Guo Ping, Lu Hanqing, Du Weimin. Pattern recognition for the classification of Raman spectroscopy signals[J]. Journal of Electronics & Information Technology, 2002, 24(6): 789-793.

Guo Ping, Lu Hanqing, Du Weimin. Pattern recognition for the classification of Raman spectroscopy signals[J]. Journal of Electronics & Information Technology, 2002, 24(6): 789-793.

Citation:

Guo Ping, Lu Hanqing, Du Weimin. Pattern recognition for the classification of Raman spectroscopy signals[J]. Journal of Electronics & Information Technology, 2002, 24(6): 789-793.

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Pattern recognition for the classification of Raman spectroscopy signals

Guo Ping^{①;卢汉清},
Lu Hanqing,
Du Weimin

Received Date: 2000-08-23
Rev Recd Date: 2001-04-23
Publish Date: 2002-06-19

Abstract

Abstract

This paper presents a study on application of regularized Gaussian classifier to Raman spectroscopy signals pattern recognition. Each Raman spectrum is treated as a point in high dimensional vector space. For given samples, the statistical model is used and Bayes decision is adopted to classify then according to their maximum posterior probabilities. Computer experiments show that the classification accuracy is obtained as high as 99.81%.

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References(1)

References

Stefan Aeberhard, Danny Coomans and Olivier de Vel, Comparative analysis of statistical pattern recognition methods in high dimensional settings, Pattern Recognition, 1994, 27(8), 1065-1077.[2]K. Fukunaga, Introduction to Statistical Pattern Recognition, second edition, Boston, Academic Press, 1990, chapter 2. ［3］J. H. Friedman, Regularized discriminant analysis, J. of American Statistics Association, 1989,84(405), 165-175.[3]Ping Guo.[J].Michael R. Lyu, Classification for high-dimension small-sample data sets based on kullback-leibler information measure, Proc. of the Int. Conference on Artificial Intelligence (ICAl2000), Las Vegas, Nevada, USA: CSREA Press.2000,:-[4]C.M. Bishop, Neural Networks for Pattern Recognition, 1995, Oxford, Oxford University Press,1995, 80-90.

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