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書目名稱Graph-Based Representation and Reasoning影響因子(影響力)




書目名稱Graph-Based Representation and Reasoning影響因子(影響力)學(xué)科排名




書目名稱Graph-Based Representation and Reasoning網(wǎng)絡(luò)公開度




書目名稱Graph-Based Representation and Reasoning網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Graph-Based Representation and Reasoning被引頻次




書目名稱Graph-Based Representation and Reasoning被引頻次學(xué)科排名




書目名稱Graph-Based Representation and Reasoning年度引用




書目名稱Graph-Based Representation and Reasoning年度引用學(xué)科排名




書目名稱Graph-Based Representation and Reasoning讀者反饋




書目名稱Graph-Based Representation and Reasoning讀者反饋學(xué)科排名

期滿

Obloquy

Graph-Based Representation and Reasoning978-3-030-23182-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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難理解

Graph-Based Variability Modelling: Towards a Classification of Existing Formalismsed options. A customised product is then derived by combining the artefacts implementing the backbone with the ones implementing the chosen options. Variability analysis and representation is a central task of this paradigm: it consists in suitably defining and structuring the scope, the commonaliti

有助于

有助于

Formal Context Generation Using Dirichlet Distributionsrithms. We suggest an improved way to randomly generate formal contexts based on Dirichlet distributions. For this purpose we investigate the predominant method, coin-tossing, recapitulate some of its shortcomings and examine its stochastic model. Building upon this we propose our Dirichlet model an

原諒

廢墟

Temporal Relations Between Imprecise Time Intervals: Representation and Reasoningcise time intervals which are classical time intervals characterized by gradual beginnings and/or endings. It is mainly based on extending the Allen’s interval algebra. It is not only suitable to express precise temporal interval relations (e.g., “Before”) but also imprecise personalized ones (e.g.,

GENRE

Relevant Attributes in Formal Contextsdeal with this, e.g., random sampling, parallelization, or attribute extraction. A so far not investigated method in the realm of formal concept analysis is attribute selection, as done in machine learning. Building up on this we introduce a method for attribute selection in formal contexts. To this