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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4735


    Title: A parallelized indexing method for large-scale case-based reasoning
    Authors: W. C. Chen;S. S. Tseng;L. P. Chang;T.P. Hong;Mon-Fong Jian
    Contributors: Department of Information Science and Applications
    Keywords: Case-based reasoning;Parallelized indexing;Bitwise indexing;Case retrieval;Performance
    Date: 2002-08
    Issue Date: 2009-11-30 16:03:20 (UTC+8)
    Publisher: Asia University
    Abstract: Case-based reasoning (CBR) is a problem-solving methodology commonly seen in artificial intelligence. It can correctly take advantage of the situations and methods in former cases to find out suitable solutions for new problems. CBR must accurately retrieve similar prior cases for getting a good performance. In the past, many researchers proposed useful technologies to handle this problem. However, the performance of retrieving similar cases may be greatly influenced by the number of cases. In this paper, the performance issue of large-scale CBR is discussed and a parallelized indexing architecture is then proposed for efficiently retrieving similar cases in large-scale CBR. Several algorithms for implementing the proposed architecture are also described. Some experiments are made and the results show the efficiency of the proposed method.
    Relation: Expert Systems with Applications 23(2):95-102
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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