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Titlebook: Applications and Science in Soft Computing; Ahamad Lotfi,Jonathan M. Garibaldi Conference proceedings 2004 Springer-Verlag Berlin Heidelbe

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樓主: Amalgam
21#
發(fā)表于 2025-3-25 03:52:29 | 只看該作者
22#
發(fā)表于 2025-3-25 07:31:55 | 只看該作者
23#
發(fā)表于 2025-3-25 11:53:06 | 只看該作者
24#
發(fā)表于 2025-3-25 16:22:03 | 只看該作者
https://doi.org/10.1007/978-3-476-03153-2r is based on the concatenation of speech units. We also use speech unit segmentation of a text for prosody modelling. The phonemes are the basic units in our neural network approach..GUHA method (General Unary Hypotheses Automaton) and a neural topology pruning process are applied for the choice of the most important input parameters.
25#
發(fā)表于 2025-3-25 23:02:01 | 只看該作者
https://doi.org/10.1007/978-3-658-14022-9ata set with no naturally occurring clusters would merely . meaningless structure. The procedure that consists in examining a data set to determine if structure is actually present and thus determine if clustering is a worthwhile operation is a poorly investigated problem known as . determination [8].
26#
發(fā)表于 2025-3-26 00:14:42 | 只看該作者
27#
發(fā)表于 2025-3-26 05:54:37 | 只看該作者
28#
發(fā)表于 2025-3-26 10:07:13 | 只看該作者
Optimisation of Neural Network Topology and Input Parameters for Prosody Modelling of Synthetic Speer is based on the concatenation of speech units. We also use speech unit segmentation of a text for prosody modelling. The phonemes are the basic units in our neural network approach..GUHA method (General Unary Hypotheses Automaton) and a neural topology pruning process are applied for the choice of the most important input parameters.
29#
發(fā)表于 2025-3-26 14:46:02 | 只看該作者
Using ART1 Neural Networks to Determine Clustering Tendencyata set with no naturally occurring clusters would merely . meaningless structure. The procedure that consists in examining a data set to determine if structure is actually present and thus determine if clustering is a worthwhile operation is a poorly investigated problem known as . determination [8].
30#
發(fā)表于 2025-3-26 17:09:16 | 只看該作者
Multistage Neural Networks: Adaptive Combination of Ensemble Results a combination function based on the results generated by the ensemble members from the first stage. A sample of the data sets from UCI Machine Learning Depository are modeled using multistage neural networks and a comparison of the performance between multistage neural networks and a majority voting scheme is conducted.
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