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Titlebook: Belief Functions: Theory and Applications; 7th International Co Sylvie Le Hégarat-Mascle,Isabelle Bloch,Emanuel Al Conference proceedings 2

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發(fā)表于 2025-3-23 11:41:59 | 只看該作者
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發(fā)表于 2025-3-23 21:51:19 | 只看該作者
https://doi.org/10.1007/978-94-011-6790-1rget recognition. In the training process, the weight of each image is automatically optimized in the networks. Finally, the performance of the proposed HIFTR has been evaluated by comparing with other related methods, and the experimental results show that the HIFTR method can efficiently improve the classification accuracy.
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發(fā)表于 2025-3-23 22:31:53 | 只看該作者
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發(fā)表于 2025-3-24 03:02:14 | 只看該作者
Ordinal Classification Using Single-Model Evidential Extreme Learning Machinenty in training labels, the proposed evidential ordinal method can be reduced to the traditional ordinal one. Experiments on artificial and UCI datasets illustrate the practical implementation and effectiveness of proposed evidential extreme learning machine for ordinal classification.
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發(fā)表于 2025-3-24 20:22:10 | 只看該作者
Themenmotivation und Gang der Untersuchung, since it can mine the ambiguity and uncertainty of the data structure; secondly, through a competitive strategy, it can automatically gain the number of clusters under the rule of intra-class compactness and inter-class dispersion. Results demonstrate the effectiveness of the proposed method on synthetic and real-world datasets.
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發(fā)表于 2025-3-24 23:12:52 | 只看該作者
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