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Titlebook: Classification - the Ubiquitous Challenge; Proceedings of the 2 Claus Weihs,Wolfgang Gaul Conference proceedings 2005 Springer-Verlag Berli

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樓主: Coagulant
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發(fā)表于 2025-3-28 17:07:09 | 只看該作者
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發(fā)表于 2025-3-28 18:48:12 | 只看該作者
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發(fā)表于 2025-3-29 00:36:12 | 只看該作者
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發(fā)表于 2025-3-29 05:15:17 | 只看該作者
https://doi.org/10.1007/978-3-319-31954-4pplications. Aggregating these to a “common” solution amounts to finding a consensus clustering, which can be characterized in a general optimization framework. We discuss recent conceptual and computational advances in this area, and indicate how these can be used for analyzing the structure in clu
45#
發(fā)表于 2025-3-29 07:55:33 | 只看該作者
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發(fā)表于 2025-3-29 11:25:47 | 只看該作者
What Drives Serendipity Research?,n of software components from component repositories and the development of components for these repositories requires an accessible information infrastructure that allows the description and comparison of these components..General knowledge relating to software development is equally important in t
47#
發(fā)表于 2025-3-29 16:41:07 | 只看該作者
48#
發(fā)表于 2025-3-29 22:45:00 | 只看該作者
Lisa N. Fink,J. César Félix-Brasdeferfactor. While valid approaches to the estimation of crude or adjusted . exist, a problem remains concerning the attribution of . to each of a set of several exposure factors. Inspired by mathematical game theory, namely, the axioms of fairness and the Shapley value, introduced by Shapley in 1953, th
49#
發(fā)表于 2025-3-30 00:06:50 | 只看該作者
María J. Barros García,Marina Terkourafiintervals for the parameters of this model, based on parametric and nonparametric bootstrap. Moreover, the label-switching problem is discussed and a solution to handle it introduced. The results are illustrated using a well-known dataset.
50#
發(fā)表于 2025-3-30 06:28:42 | 只看該作者
Researching Sociopragmatic Variabilityon sampling cases from the training set, or changing weights for cases. Reduction of classification error can also be achieved by random selection of variables to the training subsamples or directly to the model. In this paper we propose a method of feature selection for ensembles that significantly
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