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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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發(fā)表于 2025-3-26 23:00:26 | 只看該作者
32#
發(fā)表于 2025-3-27 02:55:55 | 只看該作者
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
33#
發(fā)表于 2025-3-27 07:28:57 | 只看該作者
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
34#
發(fā)表于 2025-3-27 11:56:38 | 只看該作者
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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發(fā)表于 2025-3-27 16:46:27 | 只看該作者
36#
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37#
發(fā)表于 2025-3-27 22:55:00 | 只看該作者
38#
發(fā)表于 2025-3-28 05:30:23 | 只看該作者
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.
39#
發(fā)表于 2025-3-28 07:12:38 | 只看該作者
40#
發(fā)表于 2025-3-28 10:56:51 | 只看該作者
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