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Titlebook: Advances in Self-Organizing Maps and Learning Vector Quantization; Proceedings of the 1 Thomas Villmann,Frank-Michael Schleif,Mandy Lange C

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51#
發(fā)表于 2025-3-30 09:24:56 | 只看該作者
https://doi.org/10.1007/978-1-4615-4371-8eural Gas require the use of distance metrics to measure the similarities between feature vectors as well as class prototypes. While the Euclidean distance is used in many cases, the highly correlated features within the hyperspectral representation and the high dimensionality itself favor the use o
52#
發(fā)表于 2025-3-30 15:34:30 | 只看該作者
Advances in Self-Organizing Maps and Learning Vector Quantization978-3-319-07695-9Series ISSN 2194-5357 Series E-ISSN 2194-5365
53#
發(fā)表于 2025-3-30 20:26:24 | 只看該作者
https://doi.org/10.1007/978-3-642-18012-5ectorial class labelings for the training data and the prototypes. It employs t-norms, known from fuzzy learning and fuzzy set theory, in the class label assignments, leading to a more flexible model with respect to domain requirements. We present experiments to demonstrate the extended algorithm in practice.
54#
發(fā)表于 2025-3-30 21:08:23 | 只看該作者
55#
發(fā)表于 2025-3-31 01:59:14 | 只看該作者
56#
發(fā)表于 2025-3-31 05:15:36 | 只看該作者
57#
發(fā)表于 2025-3-31 10:02:07 | 只看該作者
58#
發(fā)表于 2025-3-31 14:11:57 | 只看該作者
Probabilistic Prototype Classification Using t-normsectorial class labelings for the training data and the prototypes. It employs t-norms, known from fuzzy learning and fuzzy set theory, in the class label assignments, leading to a more flexible model with respect to domain requirements. We present experiments to demonstrate the extended algorithm in practice.
59#
發(fā)表于 2025-3-31 18:41:18 | 只看該作者
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