Laplacian minimax probability machine

K. Yoshiyama, A. Sakurai

研究成果: Article

9 引用 (Scopus)

抜粋

In this paper, we propose a Laplacian minimax probability machine, which is a semi-supervised version of minimax probability machine based on the manifold regularization framework. We also show that the proposed method can be kernelized on the basis of a theorem similar to the representer theorem for non-linear cases. Experiments confirm that the proposed methods achieve competitive results, as compared to existing graph-based learning methods such as the Laplacian support vector machine and the Laplacian regularized least square, for publicly available datasets from the UCI machine learning repository.

元の言語English
ページ(範囲)192-200
ページ数9
ジャーナルPattern Recognition Letters
37
発行部数1
DOI
出版物ステータスPublished - 2014 2 1

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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