Browsing School of Computer Science and Statistics by Author "Zenobi, Gabriele"
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Aggregating case-based reasoners in ensembles : an approach in support of explanation
Zenobi, Gabriele (Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2003)Among the reasons for the success Case-Based Reasoning (CBR) has achieved in tackling supervised learning problems, is certainly the capability to give a ranking to any case stored in the database depending on its similarity ... -
An Approach to Aggregating Ensembles of Lazy Learners that Supports Explanation
Zenobi, Gabriele; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2002-04)Ensemble research has shown that the aggregated output of an ensemble of predictors can be more accurate than a single predictor. This is true also for lazy learning systems like Case-Based Reasoning (CBR) and ... -
Case Representation Issues for Case-Based Reasoning from Ensemble Research
Cunningham, Padraig; Zenobi, Gabriele (Trinity College Dublin, Department of Computer Science, 2001-03)Ensembles of classifiers will produce lower errors than the member classifiers if there is diversity in the ensemble. One means of producing this diversity in nearest neighbour classifiers is to base the member classifiers ... -
A detailed derivation of the relationship between generalization error and ambiguity in regression ensembles
Zenobi, Gabriele (Trinity College Dublin, Department of Computer Science, 1999-12)In this technical report we will show the complete sequence of steps for the derivation of the equation of the Ensemble-Error E = E - A , introduced in the paper by Krogh and Vedelsby [1], that describes the error E of ... -
Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error
Zenobi, Gabriele; Cunningham, Padraig (Trinity College Dublin, Department of Computer Science, 2001-04)It is well known that ensembles of predictors produce better accuracy than a single predictor provided there is diversity in the ensemble. This diversity manifests itself as disagreement or ambiguity among the ...