Manifold-regularized minimax probability machine

Kazuki Yoshiyama, Akito Sakurai

研究成果: Conference contribution

4 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルPartially Supervised Learning - First IAPR TC3 Workshop, PSL 2011, Revised Selected Papers
ページ42-51
ページ数10
DOI
出版ステータスPublished - 2012 2 28
イベント1st IAPR-TC3 Workshop on Partially Supervised Learning, PSL 2011 - Ulm, Germany
継続期間: 2011 9 152011 9 16

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7081 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

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

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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