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By Dario Bini, Victor Pan (auth.), Bob F. Caviness (eds.)
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In subsequent iterations more clauses will appear until the stopping criterion is satisfied. Therefore recursive rules like the following appear: tag(A,B,art)← window(A,B,L1,L2,L3,L4,R1,R2,R3,R4),tag(A,L1,prep), tag(A,R1,n),tag(A,L2,n),tag(A,R2,virg),tag(A,L3,prep),!. tag(A,B,art)← window(A,B,L1,L2,L3,L4,R1,R2,R3,R4), word(A,B,a), tag(A,R2,prep), tag(A,R3,n),tag(A,R4,prep),!. 30 Alneu de Andrade Lopes and Alípio Jorge In general, the total number of iterations depends on the data, the language, and the underlying learning algorithm employed.
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The first four iterations use the CSC(RC1) algorithm and in the last one we use the algorithms CBR, RC1, and RC2 Figure 1 shows the coverage vs. error rate obtained using the CSC(ALG) in each iteration. In the first 4 iterations ALG is the rule learner RC1. In the last iteration we have used the algorithm CBR (Case Based Reasoning with an overlapping metric), RC1 with a new set of parameters of quality to answer the remaining cases, and RC2 (using explanations). The total number of iterations of the learning process depends on the data, the language, and the quality parameters (minimal confidence, support and selection algorithm).