English  |  正體中文  |  简体中文  |  Items with full text/Total items : 56577/90363 (63%)
Visitors : 11888026      Online Users : 68
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: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/37525

    Title: A knowledge-based approach to supervised incremental learning
    Authors: Fu, Li-min;Hsu, Hui-huang;Principe, Jose C.
    Contributors: 淡江大學資訊工程學系
    Date: 1994-06-27
    Issue Date: 2010-04-15 09:45:51 (UTC+8)
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Abstract: How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a rule-based connectionist approach in which old knowledge is preserved by bounding weight modifications. In addition, some heuristics are developed for avoiding overtraining of the network and adding new hidden units. The feasibility of this approach is demonstrated for classification problems including the iris and the promoter domains.
    Relation: Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on, vol.3, pp.1793-1798
    DOI: 10.1109/ICNN.1994.374428
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Proceeding

    Files in This Item:

    File Description SizeFormat
    078031901X_3p1793-1798.pdf393KbAdobe PDF563View/Open

    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