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Titlebook: Intelligent Information Processing VIII; 9th IFIP TC 12 Inter Zhongzhi Shi,Sunil Vadera,Gang Li Conference proceedings 2016 IFIP Internatio

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樓主: ominous
41#
發(fā)表于 2025-3-28 17:38:51 | 只看該作者
42#
發(fā)表于 2025-3-28 19:07:39 | 只看該作者
43#
發(fā)表于 2025-3-29 01:53:24 | 只看該作者
44#
發(fā)表于 2025-3-29 04:19:37 | 只看該作者
45#
發(fā)表于 2025-3-29 09:36:02 | 只看該作者
r Verbesserung der Lebensbedingungen in Stadtteilen mit besonderem Entwicklungsbedarf sollte das Programm einen Anreiz für eine verbesserte Kooperation verschiedener Ressorts setzen. Ein vergleichbarer Ansatz war zuvor in einigen Bundesl?ndern erprobt worden und brachte gro?e Erwartungen mit sich. M
46#
發(fā)表于 2025-3-29 12:35:18 | 只看該作者
An Attribute-Value Block Based Method of Acquiring Minimum Rule Sets: A Granulation Method to Constrguage and attribute-value block technique are introduced first. And then realization methods of rule reduction and rule set minimum are relatively systematically studied by using attribute-value block technique, and as a result effective algorithms of reducing decision rules and minimizing rule sets
47#
發(fā)表于 2025-3-29 19:14:16 | 只看該作者
Collective Interpretation and Potential Joint Information Maximizationmaximization has an effect to reduce the number of jointly fired neurons and then to stabilize the production of final representations. Then, the final connection weights are collectively interpreted by averaging weights produced by different data sets. The method was applied to the data set of rebe
48#
發(fā)表于 2025-3-29 22:06:39 | 只看該作者
A Novel Locally Multiple Kernel k-means Based on Similaritynel methods, the scale parameter of Gaussian kernel is usually searched in a number of candidate values of the parameter and the best is selected. In this paper, a novel multiple kernel k-means algorithm is proposed based on similarity measure. Our similarity measure meets the requirements of the cl
49#
發(fā)表于 2025-3-30 01:24:08 | 只看該作者
50#
發(fā)表于 2025-3-30 07:16:27 | 只看該作者
Application of Manifold Learning to Machinery Fault Diagnosis method to diagnose mechanical fault. Manifold Learning is widely used to extract the non-linear structure within the data and could do the dimensionality reduction of high-dimensional signal. Therefore manifold learning is employed to diagnose the machinery fault. The feature space is constructed b
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