Abstract
Support Vector Machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems. These years two-class unsupervised and semi-supervised classification algorithms based on Bounded C-SVMs, Bounded ν-SVMs, Lagrangian SVMs (LSVMs) and robust version to Bounded C − SVMs respectively, which are relaxed to Semi-definite Programming (SDP), get good classification results. But the parameter C in Bounded C-SVMs has no specific in quantification. Therefore we proposed robust version to unsupervised and semi-supervised classification algorithms based on Bounded ν− Support Vector Machines (Bν−SVMs). Numerical results confirm the robustness of proposed methods and show that our new algorithms based on robust version to Bν−SVM often obtain more accurate results than other algorithms.
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Zhao, K., Tian, Yj., Deng, Ny. (2009). Robust Unsupervised and Semi-supervised Bounded ν − Support Vector Machines . In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_36
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DOI: https://doi.org/10.1007/978-3-642-01510-6_36
Publisher Name: Springer, Berlin, Heidelberg
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