National Changhua University of Education Institutional Repository : Item 987654321/17089
English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 6507/11669
造访人次 : 29960466      在线人数 : 567
RC Version 3.2 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 进阶搜寻

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://ir.ncue.edu.tw/ir/handle/987654321/17089

题名: Credit Quality Assessments Using Manifold Based Semi-supervised Discriminant Analysis and Support Vector Machines
作者: Huang, Shian-Chang;Wu, Tung-Kuang
贡献者: 企業管理學系
日期: 2011-07
上传时间: 2013-07-11T09:05:56Z
出版者: IEEE
摘要: Due to the large scale of financial data in credit quality forecasting, dimensionality reduction is a key step to enhance classifier performance. By using manifold based semisupervised discriminant analysis (SSDA) and support vector machines, this study develops a novel prediction system for credit quality assessment, where SSDA makes efficient use of labeled and unlabeled (testing) data points to gain a perfect low dimensional approximation of data manifold and simultaneously maintain the discriminating power. More specifically, the labeled data points are used to maximize the separability between different classes, and the testing data points are used to estimate the intrinsic geometric structure of the data space. Empirical results indicate that SSDA outperforms other dimensionality reduction methods with a significant performance improvement, and our hybrid classifier substantially outperforms other conventional classifiers.
關聯: 2011 Seventh International Conference on Natural Computation, : 2037-2041
显示于类别:[企業管理學系] 會議論文

文件中的档案:

档案 大小格式浏览次数
index.html0KbHTML768检视/开启


在NCUEIR中所有的数据项都受到原著作权保护.

 

DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈