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Titlebook: Legal Issues of European Integration; Law Review of the Eu D. Gijlstra,H. G. Schermers,E. L. M. V?lker Book 1974 Springer Science+Business

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發(fā)表于 2025-3-21 17:54:45 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Legal Issues of European Integration
副標(biāo)題Law Review of the Eu
編輯D. Gijlstra,H. G. Schermers,E. L. M. V?lker
視頻videohttp://file.papertrans.cn/584/583735/583735.mp4
圖書封面Titlebook: Legal Issues of European Integration; Law Review of the Eu D. Gijlstra,H. G. Schermers,E. L. M. V?lker Book 1974 Springer Science+Business
出版日期Book 1974
關(guān)鍵詞English; Europe; European integration; competition law; university
版次1
doihttps://doi.org/10.1007/978-94-017-2061-8
isbn_softcover978-90-268-0774-9
isbn_ebook978-94-017-2061-8
copyrightSpringer Science+Business Media Dordrecht 1974
The information of publication is updating

書目名稱Legal Issues of European Integration影響因子(影響力)




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書目名稱Legal Issues of European Integration網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Legal Issues of European Integration被引頻次




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書目名稱Legal Issues of European Integration年度引用學(xué)科排名




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mple to advanced wireless power transfer concepts and system.This book presents a system-level analysis of inductive wireless power transfer (WPT) links. The basic requirements, design parameters, and utility of key building blocks used in inductive WPT links are presented, followed by detailed theo
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D. J. Gijlstra,D. F. Murphynt amounts of relevant domain-specific knowledge. The category of heuristic search techniques known as estimation of distribution algorithms (EDAs) seek to incrementally sample from probability distributions in which optimal (or near-optimal) solutions have increasingly higher probabilities. Can we
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Henry G. Schermersspect to a meta-interpreter. This paper shows how Meta-Interpretive Learning (MIL) can be extended to implement a Bayesian posterior distribution over the hypothesis space by treating the meta-interpreter as a Stochastic Logic Program. The resulting . system uses stochastic refinement to randomly sa
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spect to a meta-interpreter. This paper shows how Meta-Interpretive Learning (MIL) can be extended to implement a Bayesian posterior distribution over the hypothesis space by treating the meta-interpreter as a Stochastic Logic Program. The resulting . system uses stochastic refinement to randomly sa
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