English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 53694/88316 (61%)
造訪人次 : 10176657      線上人數 : 32
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/987654321/53759


    題名: Optimal reduction of solutions for support vector machines
    作者: Lin, Hwei-Jen;Yeh, Jih-Pin
    貢獻者: 淡江大學資訊工程學系
    關鍵詞: Support vector machine;Vector correlation;Genetic algorithms;Optimal solution;Discriminant function;Pattern recognition
    日期: 2009-08
    上傳時間: 2011-05-20 09:58:43 (UTC+8)
    出版者: Philadelphia: Elsevier Inc.
    摘要: Being a universal learning machine, a support vector machine (SVM) suffers from expensive computational cost in the test phase due to the large number of support vectors, and greatly impacts its practical use. To address this problem, we proposed an adaptive genetic algorithm to optimally reduce the solutions for an SVM by selecting vectors from the trained support vector solutions, such that the selected vectors best approximate the original discriminant function. Our method can be applied to SVMs using any general kernel. The size of the reduced set can be used adaptively based on the requirement of the tasks. As such the generalization/complexity trade-off can be controlled directly. The lower bound of the number of selected vectors required to recover the original discriminant function can also be determined.
    關聯: Applied Mathematics and Computation 214(2), pp.329-335
    DOI: 10.1016/j.amc.2009.04.010
    顯示於類別:[資訊工程學系暨研究所] 期刊論文

    文件中的檔案:

    檔案 大小格式瀏覽次數
    0096-3003_214(23)_p329-335.pdf169KbAdobe PDF175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