.png){kind=icon}
返回
Hyperparameter design criteria for support vector classifiers
.png){kind=icon}
PRE
AI
DOI：10.1016/S0925-2312(03)00430-2.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The design of a support vector machine (SVM) consists in tuning a set of hyperparameter quantities, and requires an accurate prediction of the classifier's generalization performance. The paper describes the application of the maximal-discrepancy criterion to the hyperparameter-setting process, and points out the advantages of such an approach over existing theoretical frameworks. The resulting theoretical predictions are then compared with the k-fold cross-validation empirical method, which probably is the current best-performing approach to the SVM design problem. Experimental results on a wide range of real-world testbeds prove out that the features of the maximal-discrepancy method can notably narrow the gap that so far has separated theoretical and empirical estimates of a classifier's generalization error. (C) 2003 Elsevier B.V. All rights reserved.
Keyword:
generalization error estimate
hyperparameters tuning
k-fold cross-validation
maximal-discrepancy criterion
support vector classifier
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
暂无机构信息
引用论文
A series of transition metal coordination polymers based on a rigid bi-functional carboxylate–triazolate tecton.png)
CrystEngComm
CrystEngCommIF0
PREAI
分享收藏
分享收藏
查看更多内容