淡江大學機構典藏:Item 987654321/99126
English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 62822/95882 (66%)
造訪人次 : 4019660      線上人數 : 1017
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: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/99126


    題名: Grey Support Vector Regression Model with Applications to China Tourists Forecasting in Taiwan
    作者: Tsaur, Ruey-Chyn;Chan, Shu-Feng
    貢獻者: 淡江大學管理科學學系
    關鍵詞: Grey support vector regression;grey theory;support vector regression;tourism demand forecasting
    日期: 2014-07-01
    上傳時間: 2014-10-15 14:27:38 (UTC+8)
    出版者: 新北市:淡江大學管理科學學系
    摘要: Support vector regression (SVR) has been successful in function approximation for forecasting analysis based on the idea of structural risk minimization. SVR has perfect forecasting performance by employing in large sample size for training and solving its parameters, where the SVR is difficult to be applied in limited time series data with some fluctuated points; in contrast, grey model has better forecasting performance in limited time series data. In order to cope with this problem, we use both of the advantages of support vector regression model and grey theory to construct a new grey support vector regression (GSVR) model for solving limited data with some fluctuations. Finally, we demonstrate an application for planning China tourism demand for improving the tourism infrastructure in Taiwan with a better forecasting performance.
    關聯: International Journal of Information and Management Sciences 25(2), pp.121-138
    DOI: 10.6186/IJIMS.2014.25.2.3
    顯示於類別:[管理科學學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 描述 大小格式瀏覽次數
    Grey Support Vector Regression Model with Applications to China Tourists Forecasting in Taiwan.pdf1315KbAdobe PDF31檢視/開啟
    index.html0KbHTML250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