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Considering the quality of the classroom light-environment will directly affect students' eye health and learning efficiency, it is a problem to be solved that how to evaluate the visual comfort levels of the classroom light-environment and save lighting energy on the premise of necessary visual comfort. Aiming at these problems above, this study restored the classroom scene through adjustable full-size light-environment simulation laboratory, in which 135 subjects participated in the visual comfort evaluation experiment of the indoor light-environment. After features (illuminance and correlated color temperature) and labels (comfort levels) preprocessed ,we trained and visualized the visual comfort classification models of desktop reading and blackboard reading using the algorithms of C-Support Vector Machine (C-SVM). Through the contour map and the scatter diagram, we get the classification boundary of different comfort levels and the relationship between visual comfort and lighting parameters, which has guiding significance to classroom lighting design and evaluation.

Application of C-SVM Classification Algorithm to the Lighting Visual Comfort of University Classrooms