Titlebook: Machine Learning: ECML 2000; 11th European Confer Ramon López de Mántaras,Enric Plaza Conference proceedings 2000 Springer-Verlag Berlin He

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Exploiting Classifier Combination for Early Melanoma Diagnosis Supportic training make the diagnosis by clinical inspection and they reach 80% level of both sensitivity and specificity. In this paper, we present a multi-classifiers system for supporting the early diagnosis of melanoma. The system acquires a digital image of the skin lesion and extracts a set of geomet

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A Comparison of Ranking Methods for Classification Algorithm Selectionethods for that purpose: average ranks, success rate ratios and significant wins. We also analyze the problem of evaluating and comparing these methods. The evaluation technique used is based on a leave-one-out procedure. On each iteration, the method generates a ranking using the results obtained b

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Comparing Complete and Partial Classification for Identifying Latently Dissatisfied Customersfied customers are defined as customers reporting overall satisfaction but who possess typical characteristics of dissatisfied customers. Unfortunately, identifying latenty dissatisfied customers, based on patterns of dissatisfaction, is difficult since in customer satisfaction surveys, typically on

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Boosting Applied to Word Sense Disambiguation set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy on supervised WSD. In order to make boosting practical for a real learning domain of thousands of words, several ways of accelerating th

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