Titlebook: Learning Classifier Systems; 5th International Wo Pier Luca Lanzi,Wolfgang Stolzmann,Stewart W. Wils Conference proceedings 2003 Springer-V

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0302-9743 se of accuracy-based ?tness as opposed to the more traditional strength-based ?tness. In the ?rst paper, Kovacs introduces a strength-based version of XCS, called SB-XCS. The original XCS and t978-3-540-20544-9978-3-540-40029-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

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us books have focused on the more common urologic tumors such as bladder, prostate, andkidneycancer,nonehasattemptedacomprehensivereviewofthestateoftheartofimaging in most of the tumors involved in urologic onc978-1-4471-6083-0978-1-84628-759-6

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Phillip William Dixon,Dawid Wolfe Corne,Martin John Oatesus books have focused on the more common urologic tumors such as bladder, prostate, andkidneycancer,nonehasattemptedacomprehensivereviewofthestateoftheartofimaging in most of the tumors involved in urologic onc978-1-4471-6083-0978-1-84628-759-6

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A Ruleset Reduction Algorithm for the XCS Learning Classifier System,enables classifier sets to be generated prior to passing to a reduction algorithm, allowing reliable reductions to be performed with no performance penalty. The concepts of ‘spoilers’ and ‘uncertainty’ are introduced, which help to characterise some of the peculiarities of XCS in terms of operation

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