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Titlebook: Artificial Intelligence in Theory and Practice II; IFIP 20th World Comp Max Bramer Conference proceedings 2008 IFIP International Federatio

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樓主: 尤指植物
51#
發(fā)表于 2025-3-30 09:45:24 | 只看該作者
52#
發(fā)表于 2025-3-30 15:42:38 | 只看該作者
Enhancing RBF-DDA Algorithm’s Robustness: Neural Networks Applied to Prediction of Fault-Prone Softwhine learning techniques have been used in several researches with this goal. Many recent researches use data originated from NASA (National Aeronautics and Space Administration) IV&V (Independent Verification & Validation) Facility Metrics Data Program (MDP). We have recently applied a constructive
53#
發(fā)表于 2025-3-30 17:54:14 | 只看該作者
A Study with Class Imbalance and Random Sampling for a Decision Tree Learning Systemions. Usually these methods are applied to obtain a more balanced distribution. An open-ended question about sampling methods is which distribution can provide the best results, if any. In this work we develop a broad empirical study aiming to provide more insights into this question. Our results su
54#
發(fā)表于 2025-3-30 22:26:26 | 只看該作者
55#
發(fā)表于 2025-3-31 02:28:57 | 只看該作者
56#
發(fā)表于 2025-3-31 06:12:44 | 只看該作者
57#
發(fā)表于 2025-3-31 11:15:52 | 只看該作者
Modeling Stories in the Knowledge Management Context to Improve Learning Within Organizationsods. Anyway, the complexity reached by organizations over the last years has deeply changed the role of Knowledge Management. Today, it is not possible to take care of knowledge involved in decision making processes without taking care of social context where it is produced. This point has direct im
58#
發(fā)表于 2025-3-31 13:42:03 | 只看該作者
Knowledge Modeling Framework for System Engineering Projectsdisciplinary teams. Management of knowledge emerging in previous SE projects is vital for organizational process improvement. To fully exploit this intellectual capital, it must be made explicit and shared among system engineers. In this context, we propose a knowledge modelling framework for system
59#
發(fā)表于 2025-3-31 18:03:09 | 只看該作者
Machines with good sense: How can computers become capable of sensible reasoning?as good judgment. However, in order to have good sense, people have to use common sense knowledge. This is not different to computers. Nowadays, computers are still not able to make sensible decisions and one of the reasons is the fact that they lack common sense. This paper focuses on OMCS-Br, a co
60#
發(fā)表于 2025-4-1 00:03:12 | 只看該作者
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