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Titlebook: Knowledge Discovery in Databases: PKDD 2007; 11th European Confer Joost N. Kok,Jacek Koronacki,Andrzej Skowron Conference proceedings 2007

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Site-Independent Template-Block Detection Of the many approaches proposed, most rely on the assumption of operating within the confines of a single website or require expensive hand-labeling of relevant and non-relevant blocks for model induction. This reduces their applicability, since in many practical scenarios template blocks need to b
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Statistical Model for Rough Set Approach to Multicriteria ClassificationRough Set Approach (DRSA) has been introduced to deal with the problem of multicriteria classification. However, in real-life problems, in the presence of noise, the notions of rough approximations were found to be excessively restrictive, which led to the proposal of the Variable Consistency varian
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Classification of Anti-learnable Biological and Synthetic Dataession, k-nearest neighbors, shrunken centroid, multilayer perceptron and decision trees perform in an unusual way. On certain data sets they classify a randomly sampled training subset nearly perfectly, but systematically perform worse than random guessing on cases unseen in training. We demonstrat
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An Empirical Comparison of Exact Nearest Neighbour Algorithmsarison of three prominent data structures for exact NNS: KD-Trees, Metric Trees, and Cover Trees. Our results suggest that there is generally little gain in using Metric Trees or Cover Trees instead of KD-Trees for the standard NNS problem.
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