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Titlebook: Algorithmic Learning Theory; 23rd International C Nader H. Bshouty,Gilles Stoltz,Thomas Zeugmann Conference proceedings 2012 Springer-Verla

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樓主: Coarse
21#
發(fā)表于 2025-3-25 05:50:07 | 只看該作者
22#
發(fā)表于 2025-3-25 07:32:07 | 只看該作者
Naturphilosophie und Naturwissenschaften,data mining, in which the user specifies the problem in a high level modeling language and the system automatically transforms such models into a format that can be used by a solver to efficiently generate a solution. This should be much easier for the user than having to implement or adapt an algor
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發(fā)表于 2025-3-25 14:48:42 | 只看該作者
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發(fā)表于 2025-3-25 18:35:55 | 只看該作者
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發(fā)表于 2025-3-25 21:58:12 | 只看該作者
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發(fā)表于 2025-3-26 02:32:24 | 只看該作者
0302-9743 national Conference on Algorithmic Learning Theory, ALT 2012, held in Lyon, France, in October 2012. The conference was co-located and held in parallel with the 15th International Conference on Discovery Science, DS 2012. The 23 full papers and 5 invited talks presented were carefully reviewed and s
27#
發(fā)表于 2025-3-26 04:43:14 | 只看該作者
Mathias Hildebrandt,Manfred Brockering pairwise dissimilarities and dissimilarity-based analysis of sequence data such as clustering, representative sequences, and regression trees. The tutorial also provides a short introduction to the practice of sequence analysis with the . R-package.
28#
發(fā)表于 2025-3-26 09:24:16 | 只看該作者
29#
發(fā)表于 2025-3-26 15:52:46 | 只看該作者
Naturphilosophie und Naturwissenschaften,ing. We show that in (even slightly) more complex prediction problems learnability does not imply uniform convergence. We discuss several alternative attempts to characterize learnability. This extended abstract summarizes results published in [5, 3].
30#
發(fā)表于 2025-3-26 20:35:21 | 只看該作者
https://doi.org/10.1007/978-3-531-91005-5t al. (2008). The results highlight that the selected Lasso estimator is adaptive to the smoothness of the function to be estimated, contrary to the classical Lasso or the greedy algorithm considered by Barron et al. (2008). Moreover, we prove that the rates of convergence of this estimator are optimal in the orthonormal case.
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