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Paper: Applications of Support Vector Machines in Astronomy
Volume: 485, Astronomical Data Analysis Software and Systems XXIII
Page: 239
Authors: Zhang, Y.; Zhao, Y.
Abstract: We review Support Vector Machines (SVMs) as applied in astronomy. SVMs are mainly used for solving the and regression issues. Take classification for example, selecting of cataclysmic variables from large spectroscopic survey, detecting quasar candidates from multiwavelength photometric data, identification of blue horizontal branch stars from photometric data, classification of galactic spectra, supernova search; for regression problem, photometric redshift estimation of galaxies and quasars, physical parameter measurement (metallicity, gravity, effective temperature) of stars. Comparatively, SVMs show better performance in classification than in regression. Nevertheless, SVMs has its disadvantages, which needs large computation cost on training. Based on this problem, CUDA-Accelerated SVMs is put forward. As for accuracy of SVMs, SVMs combined with other algorithms has further improvement, such as SVM-KNN.
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