Manifold-regularized minimax probability machine

Kazuki Yoshiyama, Akito Sakurai

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages42-51
Number of pages10
Volume7081 LNAI
DOIs
Publication statusPublished - 2012
Event1st IAPR-TC3 Workshop on Partially Supervised Learning, PSL 2011 - Ulm, Germany
Duration: 2011 Sep 152011 Sep 16

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7081 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other1st IAPR-TC3 Workshop on Partially Supervised Learning, PSL 2011
CountryGermany
CityUlm
Period11/9/1511/9/16

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Yoshiyama, K., & Sakurai, A. (2012). Manifold-regularized minimax probability machine. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7081 LNAI, pp. 42-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7081 LNAI). https://doi.org/10.1007/978-3-642-28258-4_5