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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4728

    Title: Learning concepts by arranging appropriate training order
    Authors: Y. T. Hsu;T. P. Hung;S. S. Tseng
    Contributors: Department of Information Science and Applications
    Date: 2001-08
    Issue Date: 2009-11-30 16:03:18 (UTC+8)
    Publisher: Asia University
    Abstract: Machine learning has been proven useful for solving the bottlenecks in building expert systems. Noise in the training instances will, however, confuse a learning mechanism. Two main steps are adopted here to solve this problem. The first step is to appropriately arrange the training order of the instances. It is well known from Psychology that different orders of presentation of the same set of training instances to a human may cause different learning results. This idea is used here for machine learning and an order arrangement scheme is proposed. The second step is to modify a conventional noise-free learning algorithm, thus making it suitable for noisy environment. The generalized version space learning algorithm is then adopted to process the training instances for deriving good concepts. Finally, experiments on the Iris Flower problem show that the new scheme can produce a good training order, allowing the generalized version space algorithm to have a satisfactory learning result.
    Relation: Minds and Machines 11(3):399-415
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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