By Agnar Aamodt (auth.), Peter Funk, Pedro A. González Calero (eds.)
This ebook constitutes the refereed lawsuits of the seventh eu convention on Case-Based Reasoning, ECCBR 2004, held in Madrid, Spain in August/September 2004.
The fifty six revised complete papers offered including an invited paper and the summary of an invited speak have been rigorously reviewed and chosen from eighty five submissions. All present matters in case-based reasoning, starting from theoretical and methodological concerns to complex functions in numerous fields are addressed.
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Extra info for Advances in Case-Based Reasoning: 7th European Conference, ECCBR 2004, Madrid, Spain, August 30 - September 2, 2004. Proceedings
The information will be used to check which method can take as input the output of this one. As example we show here the description of the CBRMethod that implements the CBR Task by decomposing it in the 4 Rs processes. , tasks with those names must have been included in the XML tasks ﬁle and a class with that name must exist in the framework.
Learning feature weights can be considered as an example of similarity maintenance. The system asks the user(s) to adjust feature weights for a set of cases, and applies the weights during case retrieval. Zhang and Yang propose a method for continually updating a feature-weighting scheme based on interactive user responses to the system’s behavior . Aha and Bankert discuss how using ﬁlter and wrapper techniques improve the classiﬁcation accuracy of their case-based classiﬁer on the cloud data set with 204 features and a few thousands data points .
Table 1. Accuracy of TA3 before and after maintenance. txt TA3X denotes application of TA3 into a domain X. 28 Niloofar Arshadi and Igor Jurisica set). The leave-one-out cross-validation (LOOCV) method was used for the lung data set, and results are averaged over 20 trials. The lung data set has also been analyzed by Jones et al. . They developed a model-based clustering prior to using the SVM classiﬁer. Their results show that the classiﬁcation accuracy of SVM prior to applying the proposed modelbased clustering is 72% using 10-fold cross-validation for evaluation.