Factorization Models for Multi-Relational Data

Language: English

Published by Cuvillier, Cuvillier Jun 2014, 2014

3954047349 / 9783954047345

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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.…

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Title
Factorization Models for Multi-Relational Data
Author
Lucas Drumond
Publisher
Cuvillier, Cuvillier Jun 2014
Publication year
2014
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3954047349
ISBN 13
9783954047345
Item weight
187 grams
Dimensions
210x148x8 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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