Variable selection in large margin classifier-based probability estimation with high-dimensional predictors

Seung Jun Shin, Yichao Wu

Research output: Contribution to journalComment/debate

3 Citations (Scopus)

Abstract

This is a discussion of the papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler.

Original languageEnglish
Pages (from-to)594-596
Number of pages3
JournalBiometrical Journal
Volume56
Issue number4
DOIs
Publication statusPublished - 2014 Jan 1
Externally publishedYes

Keywords

  • Max-type penalty
  • Regularization
  • Variable selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Variable selection in large margin classifier-based probability estimation with high-dimensional predictors'. Together they form a unique fingerprint.

  • Cite this