Titlebook: Belief Functions: Theory and Applications; 5th International Co Sébastien Destercke,Thierry Denoeux,Arnaud Martin Conference proceedings 20

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書目名稱Belief Functions: Theory and Applications影響因子(影響力)




書目名稱Belief Functions: Theory and Applications影響因子(影響力)學科排名




書目名稱Belief Functions: Theory and Applications網絡公開度




書目名稱Belief Functions: Theory and Applications網絡公開度學科排名




書目名稱Belief Functions: Theory and Applications被引頻次




書目名稱Belief Functions: Theory and Applications被引頻次學科排名




書目名稱Belief Functions: Theory and Applications年度引用




書目名稱Belief Functions: Theory and Applications年度引用學科排名




書目名稱Belief Functions: Theory and Applications讀者反饋




書目名稱Belief Functions: Theory and Applications讀者反饋學科排名

極為憤怒

裁決

小隔間

charisma

WAIL

Stamos T. Karamouzis,Stefan Feyockking process. Among the various recommendation approaches, Collaborative Filtering (CF) is considered as one of the most popular techniques in RSs. CF techniques are categorized into model-based and memory-based. Model-based approaches consist in learning a model from past ratings to perform predict

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Stavros C. Kassinos,William C. Reynoldszation where the objective is to determine the zone where the mobile sensor resides at any instant. The proposed approach uses the belief functions theory to define an evidence framework, for estimating the most probable sensor’s zone. Real experiments demonstrate the effectiveness of this approach

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Sedat Biringen,Haluk ?rs,Joel H. Ferzigerto improve their problem-solving performance and competence. However, to the best of our knowledge, all of them are not able to make use of prior knowledge which can be offered by domain experts, especially that CBR is widely applied in real-life domains. For instance, given symptoms of two differen

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