English  |  正體中文  |  简体中文  |  Items with full text/Total items : 90120/105278 (86%)
Visitors : 9010386      Online Users : 677
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/310904400/4711

    Title: A fuzzy inductive learning strategy for modular rules
    Authors: C. H. Wang;J. F. Liu;T. P. Hong;S. S. Tseng
    Contributors: Department of Information Science and Applications
    Date: 1999-04
    Issue Date: 2009-11-30 16:03:13 (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. The design of learning methods to learn concept descriptions in 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 maximum information gain is proposed to manage linguistic information. The proposed learning algorithm generates fuzzy rules from ?soft? instances, which differ from conventional instances in that they have class membership values. 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: Fuzzy Sets and System 103(1):91-105
    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
    310904400-4711.doc38KbMicrosoft Word317View/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