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影響因子(影響力)




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




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




書目名稱Machine Learning: ECML-95網(wǎng)絡公開度學科排名




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讀者反饋學科排名

吹牛者

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.

ear-canal

Enliven

NADIR

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.

討好美人

演繹

討好美人

心胸狹窄

euphoria

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.