Learning through estimating optimal formats for problem solving modules

An entire case retention in case-based reasoning is not necessarily capable of realizing what modules lead to problem-solving mal-performance. Moreover, learning through case retention calls for a subsequent case induction, which in turn may lead to a high computational cost. To circumvent this prob...

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Bibliographic Details
Published inIEEE International Conference on Systems, Man and Cybernetics Vol. 5; p. 4 pp. vol.5
Main Authors Badie, K., Reyhani, N.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2002
Subjects
Boolean functions
Computational efficiency
Computer aided software engineering
Costs
Data structures
Libraries
Problem-solving
Sliding mode control
Telecommunications
Text mining
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Summary:An entire case retention in case-based reasoning is not necessarily capable of realizing what modules lead to problem-solving mal-performance. Moreover, learning through case retention calls for a subsequent case induction, which in turn may lead to a high computational cost. To circumvent this problem a new approach to learning is proposed that concentrates on estimating the optimal formats for CBR modules before getting into the main problem-solving process. In this respect, our objective is to estimate the optimal formats for case representation, case retrieval, and solution adaptation in order to upgrade problem-solving performance for future problems. Within this context, we will demonstrate that the learning phase in CBR can itself be performed using another process of CBR.
ISBN:0780374371
9780780374379
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2002.1176381