统计研究 ›› 2005, Vol. 22 ›› Issue (11): 29-3.
• 论文 • 上一篇 下一篇
钟波; 肖智;刘朝林; 陈玲
收稿日期:
修回日期:
出版日期:
发布日期:
Received:
Revised:
Online:
Published:
关键词: credit assessment, LS-SVM, model
Abstract: Abstract:In this paper, a new method based on LS-SVM (Least Squares Support Vector Machines) is presented to deal with credit assessment in commercial banks for solving the problem of inadequate samples of the financial data, which usually happended in most banks in China. On the basis of SLT (Statistical Learning Theory), this approach with methodology of SRM (Structural Risk Minimization)wiU overcome the shortcomings of traditional credit assessment models, such as over fitting and local optimization, and, by using kernel functions in model, it will effectively solve the problems of linear inseparability and selecting parameters of model. The approach has some good properties including a generalization ability and global optimization in terms of sample processing. It is a new way for the credit assessment on the condition of small samples from bank data. The feasibility, effectiveness and practicability of presented approach was verified by experiments.
钟波, 肖智, 刘朝林, 陈玲. 基于LS-SVM的信用评价方法[J]. 统计研究, 2005, 22(11): 29-3.
0 / / 推荐
导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks
链接本文: https://tjyj.stats.gov.cn/CN/
https://tjyj.stats.gov.cn/CN/Y2005/V22/I11/29
Cited