Abstract：Multi-class Support Vector Machines (MC-SVM) based on 3 Dimension (3D) minimum within-class scatter is presented. MC-SVM based on 3D matrix patterns (MC-SVM3Dmatrix) is proposed firstly which operates inputs as 3D matrixes directly, and minimum within-class scatter SVM is adopted to design a 3D minimum within-class scatter MC-SVM. Taking advantages of both minimum within-class scatter SVM and feature of 3D space, the algorithm improves accuracy of classifiers and reduces False Positives (FP) effectively. 200-case database from Jilin Tumor Hospital is used to validate the proposed algorithm. The performances of other four CAD schemes, two radiologists and the proposed algorithm are compared on the same database. The experimental results verify the effectiveness of the proposed algorithm.
王青竹，康文炜，王新竹，王斌. 基于三维最小类内散度SVM的肺CT中的结节识别[J]. 模式识别与人工智能, 2011, 24(5): 700-706.
WANG Qing-Zhu, KANG Wen-Wei, WANG Xin-Zhu, Wang Bin. Identification of Lung Nodules in CT Images Based on 3D Minimum Within-Class Scatter SVM. , 2011, 24(5): 700-706.
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2011, Vol. 24 
