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In this article, we propose an automatic structure recovery method for generalized additive models (GAMs) by extending Wu and Stefanski's approach. In a similar vein, the proposed method is based on a local scoring algorithm coupled with local polynomial smoothing, along with a kernel‐based variable selection approach. Given a specific degree , the goal is to identify predictors contributing polynomially...
The support vector machine (SVM) is a powerful binary classification tool with high accuracy and great flexibility. It has achieved great success, but its performance can be seriously impaired if many redundant covariates are included. Some efforts have been devoted to studying variable selection for SVMs, but asymptotic properties, such as variable selection consistency, are largely unknown when...
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...
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