Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings
Bias-corrected support vector machine with Gaussian kernel in high-dimension, low-sample-size settings
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DOI：
10.1007/s10463-019-00727-1
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发表时间：
2019-07
影响因子：
1
通讯作者：
Yugo Nakayama;K. Yata;M. Aoshima
中科院分区：
文献类型：
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作者：
Yugo Nakayama;K. Yata;M. Aoshima
本文研究了高维、小样本情况下非线性支持向量机的渐近性质。我们提出了一个偏差校正支持向量机（BC-SVM），这是强大的不平衡数据在一个一般的框架。特别是，我们调查的BC-SVM具有高斯核的渐近性质，并将它们与具有线性核的比较。我们表明，BC-SVM的性能的影响所涉及的高斯核的尺度参数。我们讨论了产生高性能的尺度参数的选择，并通过数值模拟和实际数据分析来检验选择的有效性。
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.