Download Advances in Case-Based Reasoning: 7th European Conference, by Agnar Aamodt (auth.), Peter Funk, Pedro A. González Calero PDF

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 awarded 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 matters to complicated functions in a number of fields are addressed.

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Extra resources for Advances in Case-Based Reasoning: 7th European Conference, ECCBR 2004, Madrid, Spain, August 30 - September 2, 2004. Proceedings

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The performance of each CBR expert is further improved by using clustering and feature selection techniques. We apply spectral clustering [17] to cluster the data set into k groups, and the logistic regression model [18] is used to select a subset of features in each cluster. Each cluster is considered as a case-base for the k CBR experts, and the gating network learns how to combine the responses provided by each expert. Although the proposed method is applicable to any CBR system, we demonstrate the improvement achieved by applying it to a specific implementation of a CBR system, called TA3 [19].

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 [17] to cluster the data set into k groups, and the logistic regression model [18] is used to select a subset of features in each cluster. Each cluster is considered as a case-base for the k CBR experts, and the gating network learns how to combine the responses provided by each expert.

Each time before a new case is added to the case-base, the system examines the vocabulary container and suggests appropriate features that have been used previously. In this way, the vocabulary is built in parallel with the case-base. 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 [23].

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