Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine (SpringerBriefs in Computer Science)

Natarajan, Sriraam, Kersting, Kristian, Khot, Tushar, Shavli

ISBN 10: 3319136437 ISBN 13: 9783319136431

Edit� par Springer, 2015

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This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications. The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.

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Titre : Boosted Statistical Relational Learners: ...
�diteur : Springer
Date d'�dition : 2015
Reliure : Paperback
Etat : Like New
Type de livre : book

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Boosted Statistical Relational Learners

Sriraam Natarajan|Kristian Kersting|Tushar Khot|Jude Shavlik

Edit� par Springer International Publishing, 2015

ISBN 10 : 3319136437 ISBN 13 : 9783319136431

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory . N� de r�f. du vendeur 21102086

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Boosted Statistical Relational Learners | From Benchmarks to Data-Driven Medicine

Sriraam Natarajan (u. a.)

Edit� par Springer, 2015

ISBN 10 : 3319136437 ISBN 13 : 9783319136431

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Boosted Statistical Relational Learners

Sriraam Natarajan

Edit� par Springer International Publishing Mrz 2015, 2015

ISBN 10 : 3319136437 ISBN 13 : 9783319136431

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications.The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource. 84 pp. Englisch. N� de r�f. du vendeur 9783319136431

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Boosted Statistical Relational Learners

Sriraam Natarajan

Edit� par Springer International Publishing, Springer International Publishing M�r 2015, 2015

ISBN 10 : 3319136437 ISBN 13 : 9783319136431

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications.The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems.Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 84 pp. Englisch. N� de r�f. du vendeur 9783319136431

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Boosted Statistical Relational Learners : From Benchmarks to Data-Driven Medicine

Sriraam Natarajan

Edit� par Springer International Publishing, 2015

ISBN 10 : 3319136437 ISBN 13 : 9783319136431

Neuf Taschenbuch

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty. It provides an overview of the methods and the key assumptions that allow for adaptation to different models and real world applications.The models are highly attractive due to their compactness and comprehensibility but learning their structure is computationally intensive. To combat this problem, the authors review the use of functional gradients for boosting the structure and the parameters of statistical relational models. The algorithms have been applied successfully in several SRL settings and have been adapted to several real problems from Information extraction in text to medical problems. Including both context and well-tested applications, Boosting Statistical Relational Learning from Benchmarks to Data-Driven Medicine is designed for researchers and professionals in machine learning and data mining. Computer engineers or students interested in statistics, data management, or health informatics will also find this brief a valuable resource. N� de r�f. du vendeur 9783319136431

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