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    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

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