English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90453/105672 (86%)
Visitors : 12198388      Online Users : 678
RC Version 6.0 © 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://asiair.asia.edu.tw/ir/handle/310904400/4741


    Title: Fuzzy inductive learning strategies
    Authors: C. H. Wang;C. J. Tsai;T. P. Hong;S. S. Tseng
    Contributors: Department of Information Science and Applications
    Keywords: AQR algorithm;fuzzy classification;fuzzy inductive learning;machine learning;soft instances
    Date: 2003-03
    Issue Date: 2009-11-30 16:03:22 (UTC+8)
    Publisher: Asia University
    Abstract: In real applications, data provided to a learning system usually contain linguistic information which greatly influences concept descriptions derived by conventional inductive learning methods. Design of learning methods for working with vague data is thus very important. In this paper, we apply fuzzy set concepts to machine learning to solve this problem. A fuzzy learning algorithm based on the AQR learning strategy is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy linguistic rules from soft instances. Experiments on the Sports and the Iris Flower classification problems are presented to compare the accuracy of the proposed algorithm with those of some other learning algorithms. Experimental results show that the rules derived from our approach are simpler and yield higher accuracy than those from some other learning algorithms
    Relation: Applied Intelligence 18:179-193
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    0KbUnknown448View/Open
    310904400-4741.doc39KbMicrosoft Word335View/Open


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


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