Abstract
ν-Support Vector Machine is one of the most widely used support vector machines because it provides a way to control the fraction of margin errors and the fraction of support vectors. However, being biased towards the class with the more training samples prevent it from being applied to some applications in which the size of classes is uneven. Although some updated ν-SVMs have been proposed to solve this problem, there are some issues in the formulations of these updated ν-SVMs. In this paper, a new ν-SVM, Double-ν Support Vector Machine, is proposed. It introduces ν + 1 and ν − 1 to control the upper bound on the fraction of bound support vectors and the lower bound on the fraction of support vectors of the positive class and the negative class respectively. Moreover, it reduces the complexity of formulations by eliminating a redundant constrain in ν-SVM formulations.
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Yinshan, J., Yumei, W. (2006). A New Dual ν-Support Vector Machine. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_91
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DOI: https://doi.org/10.1007/11893028_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
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