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Titlebook: Discovery Science; 8th International Co Achim Hoffmann,Hiroshi Motoda,Tobias Scheffer Conference proceedings 2005 Springer-Verlag Berlin He

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發(fā)表于 2025-3-21 18:11:24 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Discovery Science
副標(biāo)題8th International Co
編輯Achim Hoffmann,Hiroshi Motoda,Tobias Scheffer
視頻videohttp://file.papertrans.cn/282/281052/281052.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Discovery Science; 8th International Co Achim Hoffmann,Hiroshi Motoda,Tobias Scheffer Conference proceedings 2005 Springer-Verlag Berlin He
出版日期Conference proceedings 2005
關(guān)鍵詞Bayesian network; LA; algorithms; autonom; classifier systems; clustering; cognition; computational learnin
版次1
doihttps://doi.org/10.1007/11563983
isbn_softcover978-3-540-29230-2
isbn_ebook978-3-540-31698-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2005
The information of publication is updating

書目名稱Discovery Science影響因子(影響力)




書目名稱Discovery Science影響因子(影響力)學(xué)科排名




書目名稱Discovery Science網(wǎng)絡(luò)公開度




書目名稱Discovery Science網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Discovery Science被引頻次




書目名稱Discovery Science被引頻次學(xué)科排名




書目名稱Discovery Science年度引用




書目名稱Discovery Science年度引用學(xué)科排名




書目名稱Discovery Science讀者反饋




書目名稱Discovery Science讀者反饋學(xué)科排名




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The Robot Scientist Projectrical tests, the Robot Scientist experiment selection methodology outperformed both randomly selecting experiments, and a greedy strategy of always choosing the experiment of lowest cost; it was also as good as the best humans tested at the task. To extend this proof of principle result to the disco
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The Arrowsmith Project: 2005 Status Report most biomedical investigators: in this case, the user chooses two literatures and then seeks to identify meaningful links between them. Could typical biomedical investigators learn to carry out Arrowsmith analyses? Would they find routine occasions for using such a sophisticated tool? Would they un
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Assisting Scientific Discovery with an Adaptive Problem Solverwith is that this learning model supposes the existence of teachers having previously solved the problem. These teachers are able to answer the learner’s queries whereas this is not the case in the context of Scientific Discovery in which it is only possible to refute a model by finding experimental
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Die Rechtfertigung der Monarchie performance guarantees (relative to their centralized or batch counterparts). It also describes how this approach can be extended to work with autonomous, and hence, inevitably semantically heterogeneous data sources, by making explicit, the ontologies (attributes and relationships between attribut
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Martina Hielscher,Uwe Laubensteinrical tests, the Robot Scientist experiment selection methodology outperformed both randomly selecting experiments, and a greedy strategy of always choosing the experiment of lowest cost; it was also as good as the best humans tested at the task. To extend this proof of principle result to the disco
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