factorization models multi relational data di drumond lucas (12 risultati)

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Paperback or Softback. Condizione: New. Factorization Models for Multi-Relational Data. Book.

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Taschenbuch. Condizione: Neu. Factorization Models for Multi-Relational Data | Lucas Drumond | Taschenbuch | 136 S. | Englisch | 2014 | Cuvillier | EAN 9783954047345 | Verantwortliche Person für die EU: Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen, info[at]cuvillier[dot]de | Anbieter: preigu.

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Mining multi-relational data has gained relevance in the last years and found applications in a number of tasks like recommender systems, link prediction, RDF mining, natural language processing, protein-interaction prediction and social network analysis just to cite a few. Appropriate machine learning models for such tasks must not only be able to operate on large scale scenarios, but also deal with noise, partial inconsistencies, ambiguities, or duplicate entries in the data. In recent years there has been a growing interest on multi-relational factorization models since they have shown to be a scalable and effective approach for multi-relational learning. This thesis formalizes the relational learning problem and investigates open issues in the state-of-the-art factorization models for multi-relational data. Specifically it studies how to deal with the open world assumption present in many real world relational datasets and how to optimize models for multiple target relations. 136 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. &Uumlber den AutorrnrnLucas Drumond was born in 1986 in Teresina, Brazil. He studied computer science and got his master degree in 2009 from the Federal University of Maranhao. In 2014, Lucas Drumond was awarded his PhD working with the Informa.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Mining multi-relational data has gained relevance in the last years and found applications in a number of tasks like recommender systems, link prediction, RDF mining, natural language processing, protein-interaction prediction and social network analysis just to cite a few. Appropriate machine learning models for such tasks must not only be able to operate on large scale scenarios, but also deal with noise, partial inconsistencies, ambiguities, or duplicate entries in the data. In recent years there has been a growing interest on multi-relational factorization models since they have shown to be a scalable and effective approach for multi-relational learning. This thesis formalizes the relational learning problem and investigates open issues in the state-of-the-art factorization models for multi-relational data. Specifically it studies how to deal with the open world assumption present in many real world relational datasets and how to optimize models for multiple target relations.Cuvillier Verlag, Nonnenstieg 8, 37075 Göttingen 136 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Mining multi-relational data has gained relevance in the last years and found applications in a number of tasks like recommender systems, link prediction, RDF mining, natural language processing, protein-interaction prediction and social network analysis just to cite a few. Appropriate machine learning models for such tasks must not only be able to operate on large scale scenarios, but also deal with noise, partial inconsistencies, ambiguities, or duplicate entries in the data. In recent years there has been a growing interest on multi-relational factorization models since they have shown to be a scalable and effective approach for multi-relational learning. This thesis formalizes the relational learning problem and investigates open issues in the state-of-the-art factorization models for multi-relational data. Specifically it studies how to deal with the open world assumption present in many real world relational datasets and how to optimize models for multiple target relations.