English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 58323/91877 (63%)
造訪人次 : 14354391      線上人數 : 172
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/64133


    題名: Financial Distress Prediction by a Radial Basis Function Network with Logit Analysis Learning
    作者: 陳慶隆;Cheng, Chi-bin;Fu, Clay C. -J.
    貢獻者: 淡江大學會計學系
    關鍵詞: Financial distress prediction;Radial basis function network;Neural networks;Logit analysis
    日期: 2006-02-01
    上傳時間: 2011-10-20 12:45:00 (UTC+8)
    摘要: This paper presents a financial distress prediction model that combines the approaches of neural network learning and logit analysis. This combination can retain the advantages and avoid the disadvantages of the two kinds of approaches in solving such a problem. The radial basis function network (RBFN) is adopted to construct the prediction model. The architecture of RBFN allows the grouping of similar firms in the hidden layer of the network and then performs a logit analysis on these groups instead of directly on the firms. Such a manner can remedy the problem of nominal variables in the input space. The performance of the proposed RBFN is compared to the traditional logit analysis and a backpropagation neural network and demonstrates superior results to both the counterparts in predictive accuracy for unseen data.
    關聯: Computers and Mathematics with Applications 51, pp.579-588
    DOI: 10.1016/j.camwa.2005.07.016
    顯示於類別:[會計學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    Financial Distress Prediction by a Radial Basis Function Network with Logit Analysis Learning.pdf777KbAdobe PDF0檢視/開啟
    index.html0KbHTML75檢視/開啟

    在機構典藏中所有的資料項目都受到原著作權保護.

    TAIR相關文章

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋