Title: Celestial Spectra Classification Based on Support Vector Machine

Year of Publication: Dec - 2017
Page Numbers: 99-105
Authors: Jingchang Pan, Gaoyu Jiang, Yude Bu, Zhenping Yi, Xin Tan
Conference Name: The Third International Conference on Computing Technology and Information Management (ICCTIM2017)
- Greece

Abstract:


Spectra clasification is essentially a pattern recognition problem, so using SVM (Support Vector Machine) to do spectral classification is feasible. In addition, the spectra of special objects can be found through the classification. In this paper, we applied the SVM method to spectra classification by Matlab simulation programs, and analyze the results of the experiments. Experimental results show the ideal classification effects.