English  |  正體中文  |  简体中文  |  Items with full text/Total items : 70548/70548 (100%)
Visitors : 23112551      Online Users : 254
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/50200


    Title: Bayesian decision theory for support vector machines: Imbalance measurement and feature optimization
    Authors: Hsu,CC;Wang,KS;Chang,SH
    Contributors: 機械工程學系
    Date: 2011
    Issue Date: 2012-03-27 17:06:17 (UTC+8)
    Publisher: 國立中央大學
    Abstract: Classification approaches usually present the poor generalization performance with an apparent class imbalance problem. Surely, a measures of the quality of the possible models reflected the remaining uncertainty in the class imbalance on learning. The purpose of our learning method is to lead an attractive pragmatic expansion scheme of the Bayesian approach to assess how well it is aligned with the class imbalance problem. Thus, we propose a method with a model assessment of the interplay between various classification decisions using probability, corresponding decision costs, and quadratic program of optimal margin classifier called: Bayesian Support Vector Machines (BSVMs) learning strategy. In the framework, we did modify in the objects and conditions of primal problem to reproduce an appropriate learning rule for an observation sample. The experiments on several existing data sets showed that BSVMs may appropriately capture the true relationship between the inputs and outputs by experimental evidence. (C) 2010 Elsevier Ltd. All rights reserved.
    Relation: EXPERT SYSTEMS WITH APPLICATIONS
    Appears in Collections:[機械工程學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML406View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明