pester
发表于 2025-3-27 00:02:51
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Flustered
发表于 2025-3-27 01:58:31
Progressive Samplingely larger samples as long as model accuracy improves. We explore several notions of efficient progressive sampling, including both methods that are asymptotically optimal and those that take into account prior expectations of appropriate data size. We then show empirically that progressive sampling indeed can be remarkably efficient.
Migratory
发表于 2025-3-27 08:57:47
Learning via Prototype Generation and Filtering the problem. We compare different variants of integration as well as existing learning algorithms such as C4.5 and KNN. Our new framework shows good performance in data reduction while maintaining or even improving classification accuracy in 19 real data sets.
意外
发表于 2025-3-27 12:32:09
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狼群
发表于 2025-3-27 16:35:53
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insomnia
发表于 2025-3-27 21:06:03
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文艺
发表于 2025-3-27 22:55:10
A Unifying View on Instance Selectioniscuss its strengths and weaknesses. Then, we propose an enhanced framework for instance selection, generic sampling, and summarize evaluation results for several instantiations of its implementation. Finally, we conclude with open issues and research challenges for instance selection as well as focusing in general.
放牧
发表于 2025-3-28 03:43:31
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vitrectomy
发表于 2025-3-28 08:43:35
The Landmark Model: An Instance Selection Method for Time Series Datats of features of landmarks, we can efficiently compute different similarity measures that are invariant under corresponding subsets of six transformations. We discuss a generalized approach for removing noise from raw time series without smoothing out the peaks and bottoms, and present a pattern representation based on the Landmark Model.
GRIEF
发表于 2025-3-28 13:55:56
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