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Titlebook: Artificial Intelligence Applications and Innovations; AIAI 2012 Internatio Lazaros Iliadis,Ilias Maglogiannis,Spyros Sioutas Conference pro

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樓主: Forestall
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發(fā)表于 2025-3-23 09:47:25 | 只看該作者
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發(fā)表于 2025-3-23 14:05:59 | 只看該作者
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發(fā)表于 2025-3-23 20:56:20 | 只看該作者
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發(fā)表于 2025-3-23 23:40:15 | 只看該作者
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發(fā)表于 2025-3-24 03:20:47 | 只看該作者
From the Database to an Expert System, services based on the heterogeneous needs and preferences of users. With the aim to enhance and support the personalization process of Web applications, an innovative adaptation mechanism is proposed. The mechanism is based on a series of psychometric measures which capture the cognitive style of u
16#
發(fā)表于 2025-3-24 06:46:18 | 只看該作者
17#
發(fā)表于 2025-3-24 12:44:29 | 只看該作者
Walter F. L. Bogaerts,Marc J. S. Vancoilletive position in the marketplace. Due to rapid changes in emerging technologies there is a need for constant improvement and adjustment to disaster recovery plans for IT systems. There are a large number of processes involved in disaster recovery planning for IT system. The interdependencies of thes
18#
發(fā)表于 2025-3-24 16:36:07 | 只看該作者
Walter F. L. Bogaerts,Marc J. S. VancoilleR is in widespread use within the pharmaceutical industry to prioritize compounds for experimental testing or to alert for potential toxicity. However, predictions from a QSAR model are difficult to assess if their prediction intervals are unknown. In this paper we introduce conformal prediction int
19#
發(fā)表于 2025-3-24 19:04:46 | 只看該作者
Walter F. L. Bogaerts,Marc J. S. Vancoilleform of clusters to a certain degree of compromise, if the examples are processed randomly without repetition in a sequential online manner? A general transductive inductive learning strategy which uses constraint based multivariate Chebyshev inequality is proposed. Theoretical convergence in the re
20#
發(fā)表于 2025-3-25 02:46:53 | 只看該作者
Walter F. L. Bogaerts,Marc J. S. Vancoilleck of Venn Predictors is their computational inefficiency, especially in the case of large datasets. In this work, we propose an Inductive Venn Predictor (IVP) which overcomes the computational inefficiency problem of the original Venn Prediction framework. Each VP is defined by a taxonomy which sep
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