English  |  正體中文  |  简体中文  |  Items with full text/Total items : 62805/95882 (66%)
Visitors : 3993338      Online Users : 299
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU 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: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/21091

    Title: Performance Enhancement of Bayesian Learning: An Application involving the Bargaining Agent of an Online Bookstore
    Other Titles: 貝氏學習績效之改善:以線上書店之議價代理人為例
    Authors: 鄭啟斌;Cheng, Chi-bin;詹智強;Chan, C. C. Henry;謝岳峰;Hsieh, Yueh-feng
    Contributors: 淡江大學資訊管理學系
    Keywords: electronic commerce;Bayesian learning;agents;bargaining
    Date: 2007-09-01
    Issue Date: 2009-11-30 13:13:34 (UTC+8)
    Publisher: 中國工業工程學會
    Abstract: E-commerce agents with Bayesian learning were first proposed by Zeng and Sycara in their Bazaar automated bargaining system [18]. Many studies have directly applied or extended Bazaar to agent learning. In Bayesian learning, it is critical to construct the conditional probabilities for new events in order to obtain an accurate estimation of the posterior probability. The construction of such conditional probabilities requires domain knowledge of the target problem and an appropriate translation of this knowledge into a corresponding set of conditional probabilities. Unfortunately, such issues have either been ignored or over-simplified in previous studies. Accordingly, the present study aims to enhance the performance of Bayesian learning by developing a new formulation for the conditional probabilities during the learning process. An online used-textbook store is built and used as the basis for a series of experiments to evaluate the performance of the proposed approach. The experimental results demonstrate that the prediction accuracy of Bayesian learning using the proposed conditional probability formulation is superior to that of a previous approach that uses a simpler formulation of conditional probabilities.
    Relation: 工業工程學刊24(5),頁388-396
    DOI: 10.1080/10170660709509054
    Appears in Collections:[資訊管理學系暨研究所] 期刊論文

    Files in This Item:

    File Description SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

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