By Agnar Aamodt (auth.), Peter Funk, Pedro A. González Calero (eds.)
This e-book constitutes the refereed court cases of the seventh ecu 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 conscientiously reviewed and chosen from eighty five submissions. All present concerns in case-based reasoning, starting from theoretical and methodological concerns to complicated functions in a variety of fields are addressed.
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Additional info for Advances in Case-Based Reasoning: 7th European Conference, ECCBR 2004, Madrid, Spain, August 30 - September 2, 2004. Proceedings
Revise. Revision is not supported by the framework. Retain. Learning as the process of updating the Case Base is delegated to the concrete implementation of the Case Base. Adding and deleting cases is parameterized with retain and forget criteria speciﬁc to the given application. Diﬀerent types of parameters for the problem solving methods are organized around the classes which map the terms in CBROnto, whose interfaces must be conform to, when extending the framework. 4 Framework Instantiation: Building a CBR System JColibri is designed to easily support the construction of CBR systems taking advantage of the task/method division paradigm described in previous sections.
More precisely: k P = i=1 k | gi | Pi T and R= i=1 | gi | Ri T The classiﬁcation error, E, is deﬁned as: k E= | gi ∩ f (gi ) |, i=1 where | gi ∩ f (gi ) | denotes the number of data points in class gi which labeled wrong, k shows the number of clusters, and f (g) is a one to one mapping from classes to clusters, such that each class gi is mapped to the cluster f (gi ). Considering the results of our comparison, we apply spectral clustering, which has been successfully used in many applications including computer vision and VLSI .
Our CBR maintenance approach has three main components: ensemble of CBR systems, clustering, and feature selection. We use an ensemble of CBR systems, called mixture of experts (MOE) to predict the classiﬁcation label of a given (input) case. A gating network calculates the weighted average of votes provided by each expert. The performance of each CBR expert is further improved by using clustering and feature selection techniques. We apply spectral clustering  to cluster the data set into k groups, and the logistic regression model  is used to select a subset of features in each cluster.
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