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Titlebook: Machine Learning and Cybernetics; 13th International C Xizhao Wang,Witold Pedrycz,Qiang He Conference proceedings 2014 Springer-Verlag Berl

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樓主: Odious
31#
發(fā)表于 2025-3-26 23:51:35 | 只看該作者
32#
發(fā)表于 2025-3-27 03:27:32 | 只看該作者
1865-0929 fereed proceedings of the 13th International Conference on Machine Learning and Cybernetics, Lanzhou, China, in July 2014. The 45 revised full papers presented were carefully reviewed and selected from 421 submissions. The papers are organized in topical sections on classification and semi-supervise
33#
發(fā)表于 2025-3-27 09:10:19 | 只看該作者
An Improved Approach to Ordinal Classificationn addition, we analyze the sensitivity of the SOCA on performance to the underlying classification algorithms, for instance, C4.5, KNN and ELM. A number of experiments are conducted and the experimental results show that the proposed method is feasible and effective.
34#
發(fā)表于 2025-3-27 09:55:57 | 只看該作者
Bandwidth Selection for Nadaraya-Watson Kernel Estimator Using Cross-Validation Based on Different Pf these four penalty functions. Then, experiments are given to compare the performance of aforementioned cross-validation methods. Finally, we give guidelines for the selection of different penalty functions in practical applications.
35#
發(fā)表于 2025-3-27 16:03:48 | 只看該作者
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發(fā)表于 2025-3-27 21:40:53 | 只看該作者
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發(fā)表于 2025-3-28 03:26:53 | 只看該作者
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發(fā)表于 2025-3-28 09:01:55 | 只看該作者
40#
發(fā)表于 2025-3-28 13:41:20 | 只看該作者
Conference proceedings 2014ification and semi-supervised learning; clustering and kernel; application to recognition; sampling and big data; application to detection; decision tree learning; learning and adaptation; similarity and decision making; learning with uncertainty; improved learning algorithms and applications.
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