Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings

Yugo Nakayama (), Kazuyoshi Yata () and Makoto Aoshima ()
Additional contact information
Yugo Nakayama: University of Tsukuba
Kazuyoshi Yata: University of Tsukuba
Makoto Aoshima: University of Tsukuba

Annals of the Institute of Statistical Mathematics, 2020, vol. 72, issue 5, No 8, 1257-1286

Abstract: Abstract In this paper, we study asymptotic properties of nonlinear support vector machines (SVM) in high-dimension, low-sample-size settings. We propose a bias-corrected SVM (BC-SVM) which is robust against imbalanced data in a general framework. In particular, we investigate asymptotic properties of the BC-SVM having the Gaussian kernel and compare them with the ones having the linear kernel. We show that the performance of the BC-SVM is influenced by the scale parameter involved in the Gaussian kernel. We discuss a choice of the scale parameter yielding a high performance and examine the validity of the choice by numerical simulations and actual data analyses.

Keywords: Geometric representation; HDLSS; Imbalanced data; Radial basis function kernel (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10463-019-00727-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:aistmt:v:72:y:2020:i:5:d:10.1007_s10463-019-00727-1

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10463/PS2

DOI: 10.1007/s10463-019-00727-1

Access Statistics for this article

Annals of the Institute of Statistical Mathematics is currently edited by Tomoyuki Higuchi

More articles in Annals of the Institute of Statistical Mathematics from Springer, The Institute of Statistical Mathematics
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().