Stellar Spectral Classification with Minimum Within-Class and Maximum Between-Class Scatter Support Vector Machine
Support Vector Machine (SVM) is one of the important stellar spectral classification methods, and it is widely used in practice. But its classification efficiencies cannot be greatly improved because it does not take the class distribution into consideration. In view of this, a modified SVM named Mi...
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| Veröffentlicht in: | Journal of astrophysics and astronomy 2016-06, Vol.37 (2), p.1-6, Article 9 |
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| container_title | Journal of astrophysics and astronomy |
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| creator | Zhong-Bao, Liu |
| description | Support Vector Machine (SVM) is one of the important stellar spectral classification methods, and it is widely used in practice. But its classification efficiencies cannot be greatly improved because it does not take the class distribution into consideration. In view of this, a modified SVM named Minimum within-class and Maximum between-class scatter Support Vector Machine (MMSVM) is constructed to deal with the above problem. MMSVM merges the advantages of Fisher’s Discriminant Analysis (FDA) and SVM, and the comparative experiments on the Sloan Digital Sky Survey (SDSS) show that MMSVM performs better than SVM. |
| doi_str_mv | 10.1007/s12036-016-9374-0 |
| format | Article |
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| ispartof | Journal of astrophysics and astronomy, 2016-06, Vol.37 (2), p.1-6, Article 9 |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Indian Academy of Sciences; Springer Nature - Complete Springer Journals |
| subjects | Artificial intelligence Astronomy Astrophysics and Astroparticles Classification Construction equipment Discriminant analysis Observations and Techniques Physics Physics and Astronomy Scatter Sky surveys (astronomy) Spectral classification Spectrum analysis Stars & galaxies Support vector machines |
| title | Stellar Spectral Classification with Minimum Within-Class and Maximum Between-Class Scatter Support Vector Machine |
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