15 November 2007 Research on multi-class classification of support vector data description
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Proceedings Volume 6788, MIPPR 2007: Pattern Recognition and Computer Vision; 678829 (2007); doi: 10.1117/12.751217
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China
Abstract
Support Vector Data Description (SVDD) is a one-class classification method developed in recent years. It has been used in many fields because of its good performance and high executive efficiency when there are only one-class training samples. It has been proven that SVDD has less support vector numbers, less optimization time and faster testing speed than those of two-class classifier such as SVM. At present, researches and acquirable literatures about SVDD multi-class classification are little, which restricts the SVDD application. One SVDD multi-class classification algorithm is proposed in the paper. Based on minimum distance classification rule, the misclassification in multi-class classification is well solved and by applying the threshold strategy the rejection in multi-class classification is greatly alleviated. Finally, by classifying range profiles of three targets, the effect of kernel function parameter and SNR on the proposed algorithm is investigated and the effectiveness of the algorithm is testified by quantities of experiments.
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Minghua Shen, Huaitie Xiao, Qiang Fu, "Research on multi-class classification of support vector data description", Proc. SPIE 6788, MIPPR 2007: Pattern Recognition and Computer Vision, 678829 (15 November 2007); doi: 10.1117/12.751217; https://doi.org/10.1117/12.751217
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Signal to noise ratio

Detection and tracking algorithms

Optical spheres

Radar

Computer simulations

Control systems

Data acquisition

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