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

Seung Jun Shin; Yichao Wu

doi:10.1002/bimj.201300251

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

Biom J. 2014 Jul;56(4):594-6. doi: 10.1002/bimj.201300251. Epub 2014 Feb 25.

Authors

Seung Jun Shin¹, Yichao Wu

Affiliation

¹ Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

PMID: 24615809
DOI: 10.1002/bimj.201300251

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.

Keywords: Max-type penalty; Regularization; Variable selection.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Artificial Intelligence*
Probability*
Support Vector Machine

Grants and funding

R01-CA149569/CA/NCI NIH HHS/United States