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Titlebook: Artificial Intelligence Applications and Innovations; 3rd IFIP Conference Ilias Maglogiannis,Kostas Karpouzis,Max Bramer Conference procee

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樓主: Annihilate
21#
發(fā)表于 2025-3-25 05:23:44 | 只看該作者
Intelligent Configurable Electronic Shop Platform based on Ontologies and 3D Visualization,sible visualization outcome. The platform, designed for the furniture sector, includes all the practicable electronic commerce variants and its on-line product configuration process is controlled by an ontology that was created using the OWL Web Ontology Language.
22#
發(fā)表于 2025-3-25 11:18:44 | 只看該作者
Reliability and Validity in Expert Judgmentinary class problems that encode the ordering of the original classes. The paper concludes that the proposed technique can be a more robust solution to the problem because it minimizes the distances between the actual and the predicted classes as well as improves the classification accuracy.
23#
發(fā)表于 2025-3-25 12:25:56 | 只看該作者
Mechanisms of Institutional?Couplinglacks generalization, which means that new points cannot be added to the obtained map without recalculating it. The SAMANN network offers the generalization ability of projecting new data, which is not present in the original Sammon’s projection algorithm. Retraining of the network when the new data points appear has been analyzed in this paper.
24#
發(fā)表于 2025-3-25 19:28:23 | 只看該作者
Mechanisms of Institutional?Couplingool — the executable Modeling Framework (XMF). The example is that of modeling a knowledge-based system for the Ulcer Clinical Practical Guidelines (CPG) Recommendations. It demonstrates the use of the profile, with the prototype system implemented in the Java Expert System Shell (JESS).
25#
發(fā)表于 2025-3-25 23:08:45 | 只看該作者
26#
發(fā)表于 2025-3-26 04:11:39 | 只看該作者
27#
發(fā)表于 2025-3-26 07:33:27 | 只看該作者
Local Ordinal Classification,inary class problems that encode the ordering of the original classes. The paper concludes that the proposed technique can be a more robust solution to the problem because it minimizes the distances between the actual and the predicted classes as well as improves the classification accuracy.
28#
發(fā)表于 2025-3-26 10:14:21 | 只看該作者
Retraining the Neural Network for Data Visualization,lacks generalization, which means that new points cannot be added to the obtained map without recalculating it. The SAMANN network offers the generalization ability of projecting new data, which is not present in the original Sammon’s projection algorithm. Retraining of the network when the new data points appear has been analyzed in this paper.
29#
發(fā)表于 2025-3-26 16:26:37 | 只看該作者
30#
發(fā)表于 2025-3-26 19:56:14 | 只看該作者
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