TY - GEN
T1 - Manifold-regularized minimax probability machine
AU - Yoshiyama, Kazuki
AU - Sakurai, Akito
PY - 2012/2/28
Y1 - 2012/2/28
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84857411643&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84857411643&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-28258-4_5
DO - 10.1007/978-3-642-28258-4_5
M3 - Conference contribution
AN - SCOPUS:84857411643
SN - 9783642282577
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 42
EP - 51
BT - Partially Supervised Learning - First IAPR TC3 Workshop, PSL 2011, Revised Selected Papers
T2 - 1st IAPR-TC3 Workshop on Partially Supervised Learning, PSL 2011
Y2 - 15 September 2011 through 16 September 2011
ER -