Laplacian minimax probability machine

K. Yoshiyama, A. Sakurai

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)192-200
Number of pages9
JournalPattern Recognition Letters
Volume37
Issue number1
DOIs
Publication statusPublished - 2014 Feb 1

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Support vector machines
Learning systems
Experiments

Keywords

  • Laplacian RLS
  • Laplacian SVM
  • Manifold regularization
  • Minimax probability machine
  • Semi-supervised learning

ASJC Scopus subject areas

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

Cite this

Laplacian minimax probability machine. / Yoshiyama, K.; Sakurai, A.

In: Pattern Recognition Letters, Vol. 37, No. 1, 01.02.2014, p. 192-200.

Research output: Contribution to journalArticle

Yoshiyama, K. ; Sakurai, A. / Laplacian minimax probability machine. In: Pattern Recognition Letters. 2014 ; Vol. 37, No. 1. pp. 192-200.
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