Semi-supervised logistic discrimination via graph-based regularization

Shuichi Kawano, Toshihiro Misumi, Sadanori Konishi

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

We address the problem of constructing a nonlinear discriminant procedure based on both labeled and unlabeled data sets.Asemi-supervised logistic model with Gaussian basis functions is presented along with the technique of graph-based regularization. A crucial issue in modeling process is the choice of tuning parameters included in the nonlinear semisupervised logistic models. In order to select these adjusted parameters, we derive model selection criteria from the viewpoints of information theory and also the Bayesian approach. Some numerical examples are given to investigate the effectiveness of our proposed semisupervised modeling strategies.

Original languageEnglish
Pages (from-to)203-216
Number of pages14
JournalNeural Processing Letters
Volume36
Issue number3
DOIs
Publication statusPublished - Dec 2012
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • General Neuroscience
  • Computer Networks and Communications
  • Artificial Intelligence

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