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Titlebook: Machine Learning: ECML-95; 8th European Confere Nada Lavrac,Stefan Wrobel Conference proceedings 1995 Springer-Verlag Berlin Heidelberg 199

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書目名稱Machine Learning: ECML-95
副標(biāo)題8th European Confere
編輯Nada Lavrac,Stefan Wrobel
視頻videohttp://file.papertrans.cn/621/620758/620758.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Machine Learning: ECML-95; 8th European Confere Nada Lavrac,Stefan Wrobel Conference proceedings 1995 Springer-Verlag Berlin Heidelberg 199
描述This volume constitutes the proceedings of the Eighth European Conference on Machine Learning ECML-95, held in Heraclion, Crete in April 1995..Besides four invited papers the volume presents revised versions of 14 long papers and 26 short papers selected from a total of 104 submissions. The papers address all current aspects in the area of machine learning; also logic programming, planning, reasoning, and algorithmic issues are touched upon.
出版日期Conference proceedings 1995
關(guān)鍵詞Case-Based Learning; Concept-Learning; Fallbasiertes Lernen; Genetische Algorithmen; Lern-Algorithmen; Ma
版次1
doihttps://doi.org/10.1007/3-540-59286-5
isbn_softcover978-3-540-59286-0
isbn_ebook978-3-540-49232-0Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 1995
The information of publication is updating

書目名稱Machine Learning: ECML-95影響因子(影響力)




書目名稱Machine Learning: ECML-95影響因子(影響力)學(xué)科排名




書目名稱Machine Learning: ECML-95網(wǎng)絡(luò)公開度




書目名稱Machine Learning: ECML-95網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Machine Learning: ECML-95被引頻次




書目名稱Machine Learning: ECML-95被引頻次學(xué)科排名




書目名稱Machine Learning: ECML-95年度引用




書目名稱Machine Learning: ECML-95年度引用學(xué)科排名




書目名稱Machine Learning: ECML-95讀者反饋




書目名稱Machine Learning: ECML-95讀者反饋學(xué)科排名




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Multiple-Knowledge Representations in concept learning,owledge level to build an . correct . description of it. This approach is illustrated through our GEM system which learns concepts in a numerical attribute space using a Neural Network representation as the deep knowledge level and symbolic rules as the shallow level.
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Learning abstract planning cases,troduced model. An empirical study in the domain of process planning in mechanical engineering shows significant improvements in planning efficiency through learning abstract cases while an explanation-based learning method only causes a very slight improvement.
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Analogical logic program synthesis from examples,d, two programs are said to be similar if they share a common explanation structure at an abstract level. Using this notion of similarity, we formalize an analogical logic program synthesis and show that our algorithm based on a framework of model inference can identify a desired program.
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