Manifold-Regularized Minimax Probability Machine

Manifold-Regularized Minimax Probability Machine
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DOI：
10.1007/978-3-642-28258-4_5
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发表时间：
2011-09
期刊：
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影响因子：
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通讯作者：
K. Yoshiyama;A. Sakurai
K. Yoshiyama;A. Sakurai
中科院分区：
其他
文献类型：
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作者：
K. Yoshiyama;A. Sakurai

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本文提出了流形正则化极大极小概率机，简称MRMPM。证明了极大极小概率机在流形正则化框架下可以适当地推广为半监督形式，并在非线性情况下得到了其核化形式。我们的实验表明，对于UCI机器学习存储库中公开可用的数据集，所提出的方法获得的结果与现有的学习方法（如拉普拉斯支持向量机和拉普拉斯正则化最小二乘）相竞争。
In this paper we propose Manifold-Regularized Minimax Probability Machine, called MRMPM. We show that Minimax Probability Machine can properly be extended to semi-supervised version in the manifold regularization framework and that its kernelized version is obtained for non-linear case. Our experiments show that the proposed methods achieve results competitive to existing learning methods, such as Laplacian Support Vector Machine and Laplacian Regularized Least Square for publicly available datasets from UCI machine learning repository.