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Titlebook: Advances in Computational Collective Intelligence; 14th International C Costin B?dic?,Jan Treur,Marek Krótkiewicz Conference proceedings 20

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樓主: Traction
41#
發(fā)表于 2025-3-28 16:38:59 | 只看該作者
42#
發(fā)表于 2025-3-28 21:23:14 | 只看該作者
SSTop3: Sole-Top-Three and Sum-Top-Three Class Prediction Ensemble Method Using Deep Learning Classireatment of the subject; decadence involves a policy of conscious exclusion in order to produce an artificial state in the beholder of what Vladimir Nabokov called ‘a(chǎn)esthetic bliss’, the sense of a job well done, a kick well administered. ‘Salome Dancing’ is a choice and memorable picture, and it is
43#
發(fā)表于 2025-3-29 01:32:04 | 只看該作者
44#
發(fā)表于 2025-3-29 03:44:53 | 只看該作者
An Opinion Analysis Method Based on Disambiguation to Improve a Recommendation System978-1-349-02166-6
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發(fā)表于 2025-3-29 08:36:31 | 只看該作者
46#
發(fā)表于 2025-3-29 12:00:40 | 只看該作者
47#
發(fā)表于 2025-3-29 17:58:11 | 只看該作者
Ade Oyedijo,Kweku Adams,Serge Koukpakiel augmentation-based, ensemble learning approach that can be seen as a mixture between stacking and bagging. The proposed approach leverages text augmentation to enhance base learners’ diversity and accuracy, ergo the predictive performance of the ensemble. Experiments conducted on two real-world d
48#
發(fā)表于 2025-3-29 21:23:46 | 只看該作者
Ade Oyedijo,Kweku Adams,Serge Koukpakiesent after a comparative study between the methods proposed in the literature a method to do the disambiguation before going through the classification phase which we have done using the SVM algorithm. Our proposed system gives its best result in terms of accuracy 97.1%.
49#
發(fā)表于 2025-3-30 00:21:56 | 只看該作者
The Competitive Advantage of Informationpresent our new application for annotating travel offers, prepared in a human-in-the-loop paradigm that enables iterative system improvement. We also show a large dataset containing more than 3,000 manually constructed queries and more than 23,000 manually annotated answers, a large fraction by at l
50#
發(fā)表于 2025-3-30 07:51:57 | 只看該作者
https://doi.org/10.1057/9780230509337tian, and Lebanese). Secondly, we propose a new approach for inappropriate content detection. Our approach is based on a combination of AraVec and fastText word embedding as input features and a Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) model. The obtained results s
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