v-Structural Nonparallel Support Vector Machine for Pattern Classification
Dandan Chen, Yingjie Tian · 2016
The ν-nonparallel support vector machine (ν-SNPSVM) for classification has the advantage of using a parameter ν on controlling the number of support vectors. However, it ignores the prior structural information in data. In this paper, we propose a novel nonparallel classifier, named ν-Structural Nonparallel Support Vector Machine (ν-SNPSVM), for binary classification. Each model of ν-SNPSVM considers not only the compactness in both classes by the structural information but also the separability between classes, thus it can fully exploit prior knowledge to directly improve the algorithm's generalization capacity and is certainly superior to NPSVMs and other twin SVMs theoretically. Experimental results on lots of data sets show the effectiveness of our method.