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Titlebook: Bio-Inspired Systems: Computational and Ambient Intelligence; 10th International W Joan Cabestany,Francisco Sandoval,Juan M. Corchado Confe

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41#
發(fā)表于 2025-3-28 16:49:11 | 只看該作者
42#
發(fā)表于 2025-3-28 21:57:19 | 只看該作者
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發(fā)表于 2025-3-29 00:56:56 | 只看該作者
44#
發(fā)表于 2025-3-29 05:18:03 | 只看該作者
45#
發(fā)表于 2025-3-29 09:18:11 | 只看該作者
Die Katharsis im psychodramatischen Spiel,s a simple remedy for overcoming the slowdown in the learning process incurred by TM. This is based on slightly perturbing the values of the training examples, so that they are no longer symmetric. Simulations demonstrate the usefulness of this approach.
46#
發(fā)表于 2025-3-29 14:44:39 | 只看該作者
47#
發(fā)表于 2025-3-29 19:05:44 | 只看該作者
A Simple Maximum Gain Algorithm for Support Vector Regressionstarting multipliers. We will numerically show that the proposed procedure requires essentially the same number of iterations than Modification 2 having thus the same time performance while being much simpler to code.
48#
發(fā)表于 2025-3-29 21:40:54 | 只看該作者
An Awareness-Based Artificial Neural Network for Cooperative Distributed Environmentschniques. It presents a novel non-supervised Artificial Neural Network (ANN) based model for CDE named CAwANN, which uses the information of awareness collaborations occurring in the environment in order to achieve the most suitable awareness-based collaboration / cooperation in the environment.
49#
發(fā)表于 2025-3-30 00:52:56 | 只看該作者
Improving Classification under Changes in Class and Within-Class Distributionsecomposed in several (unknown) subclasses, and (b) the prior subclass probabilities may change after training. Experimental results with practical applications show an improvement over an adaptive method based on class priors, while preserving a similar performance when there are no subclass changes.
50#
發(fā)表于 2025-3-30 06:52:37 | 只看該作者
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