.png){kind=icon}
返回
The trace kernel bandwidth criterion for support vector data description
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2020.107662.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Support vector data description (SVDD) is a popular anomaly detection technique. The computation of the SVDD classifier requires a kernel function, for which the Gaussian kernel is a common choice. The Gaussian kernel has a bandwidth parameter, and it is important to set the value of this parameter correctly to ensure good results. A small bandwidth leads to overfitting, and the resulting SVDD classifier overestimates the number of anomalies, whereas a large bandwidth leads to underfitting and an inability to detect many anomalies. In this paper, we present a new, unsupervised method for selecting the Gaussian kernel bandwidth. Our method exploits a low-rank representation of the kernel matrix to suggest a kernel bandwidth value. Our new technique is competitive with the current state of the art for low dimensional data and performs extremely well for many classes of high-dimensional data. This method is also applicable to one-class support vector machines (OCSVM). (C) 2020 Elsevier Ltd. All rights reserved.
Keyword:
Support vector data description
SVDD
One-class support vector machines
OCSVM
Gaussian kernel
Automatic tuning
Gaussian kernel bandwidth
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.6
论文数:
1.3W
被引数:
4.5W
机构
引用论文
One-class classification for mapping a specific land-cover class: SVDD classification of fenland
OAAI
OAAI分享收藏Two methods of selecting Gaussian kernel parameters for one-class SVM and their application to fault detection一类支持向量机的两种高斯核参数选择方法及其在故障检测中的应用
PREAI
分享收藏分享收藏General support vector representation machine for one-class classification of non-stationary classes.png)
PATTERN RECOGNITION
OAAI
PATTERN RECOGNITIONIF7.6
OAAI分享收藏
查看更多内容