Abstract
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.
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Yoshiyama, K., Sakurai, A. (2012). Manifold-Regularized Minimax Probability Machine. In: Schwenker, F., Trentin, E. (eds) Partially Supervised Learning. PSL 2011. Lecture Notes in Computer Science(), vol 7081. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28258-4_5
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DOI: https://doi.org/10.1007/978-3-642-28258-4_5
Publisher Name: Springer, Berlin, Heidelberg
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