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Titlebook: Research and Development in Intelligent Systems XXXI; Incorporating Applic Max Bramer,Miltos Petridis Conference proceedings 2014 Springer

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21#
發(fā)表于 2025-3-25 04:06:57 | 只看該作者
Alexandros Gkiokas,Alexandra I. Cristea,Matthew Thorpeopologist Bronislaw Malinowski (1884–1942), according to a ‘two-person, single career’ model (Papanek, American Journal of Sociology, 78:852–72, 1973) based on gender asymmetry. Thereby, the article introduces the main themes of my two-direction ongoing research on Elise Masson’s whole work and on h
22#
發(fā)表于 2025-3-25 08:03:21 | 只看該作者
Conference proceedings 2014 volume in the .Research and Development in Intelligent Systems. series, which also incorporates the twenty-second volume in the .Applications and Innovations in Intelligent Systems. series. These series are essential reading for those who wish to keep up to date with developments in this important field..
23#
發(fā)表于 2025-3-25 13:29:01 | 只看該作者
Chris Jones,Malcolm Crowe counter-strategies. The book will be of use to students and scholars interested in gender and politics, European politics, and political extremism..978-3-319-82849-7978-3-319-43533-6Series ISSN 2662-5814 Series E-ISSN 2662-5822
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發(fā)表于 2025-3-25 19:23:15 | 只看該作者
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發(fā)表于 2025-3-26 00:02:50 | 只看該作者
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發(fā)表于 2025-3-26 02:17:34 | 只看該作者
27#
發(fā)表于 2025-3-26 06:12:12 | 只看該作者
orates the twenty-second volume in the .Applications and Innovations in Intelligent Systems. series. These series are essential reading for those who wish to keep up to date with developments in this important field..978-3-319-12068-3978-3-319-12069-0
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發(fā)表于 2025-3-26 08:42:23 | 只看該作者
29#
發(fā)表于 2025-3-26 16:37:56 | 只看該作者
Reluctant Reinforcement Learningm the multiple use of a small quantity of previous experiences to validate updates to the strategy moves the MLP towards convergence and finds a balance between exploration of improvements to strategy and exploitation of previous learning.
30#
發(fā)表于 2025-3-26 18:27:17 | 只看該作者
Preference and Sentiment Guided Social Recommendations with Temporal Dynamicsting a comparative analysis on data extracted from Amazon.com and Cnet. We show that considering the temporal preferences of users leads to better recommendation and that user preferences change over time.
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