期刊
ELECTRONICS
卷 12, 期 3, 页码 -出版社
MDPI
DOI: 10.3390/electronics12030645
关键词
support vector machine; structural information; Earth mover's distance; deep convolutional feature; pattern recognition
An improved support vector machine with earth mover's distance (EMD-SVM) is proposed, which considers the data structure information and automatically learns the distribution between classes. Experimental validation demonstrates its superior and robust performance.
A support vector machine (SVM) aims to achieve an optimal hyperplane with a maximum interclass margin and has been widely utilized in pattern recognition. Traditionally, a SVM mainly considers the separability of boundary points (i.e., support vectors), while the underlying data structure information is commonly ignored. In this paper, an improved support vector machine with earth mover's distance (EMD-SVM) is proposed. It can be regarded as an improved generalization of the standard SVM, and can automatically learn the distribution between the classes. To validate its performance, we discuss the necessity of the structural information of EMD-SVM in the linear and nonlinear cases, respectively. Experimental validation was designed and conducted in different application fields, which have shown its superior and robust performance.
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