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Titlebook: Artificial Neural Networks - ICANN 2008; 18th International C Véra K?rková,Roman Neruda,Jan Koutník Conference proceedings 2008 Springer-Ve

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61#
發(fā)表于 2025-4-1 02:46:02 | 只看該作者
Radiazione ambientale naturale, the fact that the MACs come for free as these are FPGA’s built-in cores. The hardware is as fast as existing ones as it is massively parallel. Besides, the proposed hardware can adjust itself on-the-fly to the user-defined topology of the neural network, with no extra configuration, which is a very
62#
發(fā)表于 2025-4-1 10:02:05 | 只看該作者
A Model-Based Relevance Estimation Approach for Feature Selection in Microarray Datasetsombines the low-bias relevance estimator with state-of-the-art relevance estimators in order to enhance their accuracy. The experimental validation on 20 publicly available cancer expression datasets shows the robustness of a selection approach which is not biased by a specific learner.
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發(fā)表于 2025-4-1 12:51:35 | 只看該作者
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發(fā)表于 2025-4-1 18:07:27 | 只看該作者
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發(fā)表于 2025-4-1 22:21:44 | 只看該作者
Investigating Similarity of Ontology Instances and Its Causesge information spaces. It is based on the assumption that comparing attributes of documents which were found interesting for a user can be a source for discovering information about user’s interests. We consider applications for the Semantic Web where documents or their parts are represented by onto
66#
發(fā)表于 2025-4-2 01:17:02 | 只看該作者
A Neural Model for Delay Correction in a Distributed Control Systemn some distributed control systems it is possible to know, at control time, the value of the delay. The work reported in this paper proposes to build a model of the behavior of the system in the presence of the variable delay and to use this model to compensate the control signal in order to avoid t
67#
發(fā)表于 2025-4-2 06:11:20 | 只看該作者
A Model-Based Relevance Estimation Approach for Feature Selection in Microarray Datasetsproaches to feature selection are generally denoted as wrappers. Wrapper methods assess subsets of variables according to their usefulness to a given prediction model which will be eventually used for classification. This strategy assumes that the accuracy of the model used for the wrapper selection
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