By Sholom M. Weiss, Nitin Indurkhya (auth.), Maria Carolina Monard, Jaime Simão Sichman (eds.)
This yr, Brazil celebrates its 500 years of discovery. To mark this nice occasion, the Brazilian Arti cial Intelligence (AI) group prepared a unique int- nationwide joint convention placing jointly SBIA 2000 (the Brazilian AI Sym- sium) and IBERAMIA 2000 (the Ibero-American AI Conference). SBIA 2000 is the fifteenth convention of the SBIA convention sequence, that is the best convention in Brazil for presentation of study and purposes in Arti cial Intelligence. considering that 1995, SBIA has turn into a global convention, with papers written in English, a global application committee, and p- ceedings released in Springer-Verlag’s Lecture Notes in Arti cial Intelligence (LNAI) sequence. IBERAMIA 2000 is the seventh convention of the IBERAMIA convention sequence, which has been essentially the most appropriate boards for ibero-american AI rese- chers (from South and crucial the USA, Mexico, Spain, and Portugal) to provide their effects. Following the SBIA and EPIA (Portuguese convention on AI) ex- riences, from IBERAMIA’98 on, it has additionally develop into a global convention, with court cases released in Springer-Verlag’s LNAI series.
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Additional info for Advances in Artificial Intelligence: International Joint Conference 7th Ibero-American Conference on AI 15th Brazilian Symposium on AI IBERAMIA-SBIA 2000 Atibaia, SP, Brazil, November 19–22, 2000 Proceedings
Therefore, to classify a new case, it looks for relevant stored explanations applicable to the new case. The different possible views of the case given by the explanations correspond to considering different sets of conditions/features to analyze the case. In other words, they lead to different ways to compute similarity between known cases/explanations and the new case to be classified (as opposed to the commonly used fixed metric). 1 Introduction The integration of rules and cases for classification tasks is a recurrent theme in machine learning (Domingos, 1996; Golding & Rosenbloom 1996).
De Andrade Lopes and A. Jorge Coverage x Error 0,25 0,2 CBR 0,15 Error 0,1 RC1 RC2 0,05 0 50 60 70 80 90 100 Coverage % Fig. 1. Coverage Error. 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.
Experience Factory. In J. J. ), Encyclopedia of Software Engineering, John Wiley & Sons (1994) 6. Basili, V. , Rombach, H. : Goal Question Metric Paradigm. In J. J. ), Encyclopedia of Software Engineering, John Wiley & Sons (1994) 7. CEMP Consortium. Customized Establishment of Measurement Programs. 10358 (1996) 8. : Intelligent Retrieval of Software Engineering Experienceware. Proc. of the 11th Int. Conf. on Software Engineering and Knowledge Engineering, Germany (1999) 9. : REMEX - A Case-Based Approach for Reuse of Software Measurement Experienceware.