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Titlebook: Artificial Intelligence and Soft Computing – ICAISC 2006; 8th International Co Leszek Rutkowski,Ryszard Tadeusiewicz,Jacek M. ?ur Conferenc

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41#
發(fā)表于 2025-3-28 18:05:30 | 只看該作者
42#
發(fā)表于 2025-3-28 20:19:08 | 只看該作者
43#
發(fā)表于 2025-3-29 01:32:14 | 只看該作者
Bruce Simmons,Robyn Bushell,Jennifer Scottew data points appear has been analyzed in this paper. Two strategies for retraining the neural network that realizes the multidimensional data visualization have been proposed and then the analysis has been made.
44#
發(fā)表于 2025-3-29 06:46:48 | 只看該作者
Facilitating Visual Socialities addition, the database extends classical equivalence relations with fuzzy proximity relations, which provide users with interesting analytical capabilities. In this paper we concentrate on both of these properties when proposing new approaches to interpretation of non-atomic values for decision making purposes.
45#
發(fā)表于 2025-3-29 08:57:12 | 只看該作者
Artificial Intelligence and Soft Computing – ICAISC 2006978-3-540-35750-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
46#
發(fā)表于 2025-3-29 12:47:02 | 只看該作者
0302-9743 Overview: 978-3-540-35748-3978-3-540-35750-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
47#
發(fā)表于 2025-3-29 19:17:48 | 只看該作者
48#
發(fā)表于 2025-3-29 19:56:12 | 只看該作者
https://doi.org/10.1007/978-94-007-6137-7function networks. The parameters of the network including centers, covariance matrices and synaptic weights are trained by the empirical risk minimization. We show the rates of convergence for the networks whose parameters are learned by the complexity regularization.
49#
發(fā)表于 2025-3-30 02:20:11 | 只看該作者
Systems of Care in North Americaus CNNs. According to our simulation result for the single neuron model, this new C-CNN model has richer and more flexible dynamics, compared to the conventional CNN with only stable dynamics. The hardware implementation of this new network may be important for solving a wide variety of combinatorial optimization problems.
50#
發(fā)表于 2025-3-30 05:45:28 | 只看該作者
James Y. L. Thong,Chee-Sing Yapwith a metric in the label space. Then, this problem is approximately solved, providing hints on selecting appropriate RBF nets. It was shown that the approximate solution is the exact one in several important cases. Finally, we propose the algorithm for learning the proposed RBF net. The results of its testing are briefly reported.
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