Robust Unsupervised and Semi-supervised Bounded ν - Support Vector Machines

Authors:
Kun Zhao;Ying-Jie Tian;Nai-Yang Deng
Affiliations:
Logistics School, Beijing Wuzi University, Beijing, China 101149;Research Center on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China 100190;College of Science, China Agricultural University, Beijing, China 100083
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
Year:
2009

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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 ν 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.