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- Title
Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings.
- Authors
Nakayama, Yugo; Yata, Kazuyoshi; Aoshima, Makoto
- 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.
- Subjects
SUPPORT vector machines; KERNEL functions; RADIAL basis functions; DATA analysis; COMPUTER simulation
- Publication
Annals of the Institute of Statistical Mathematics, 2020, Vol 72, Issue 5, p1257
- ISSN
0020-3157
- Publication type
Article
- DOI
10.1007/s10463-019-00727-1