9783319136431 - boosted statistical relational learners: from benchmarks to data-driven medicine di natarajan, sriraam (14 risultati)
Boosted Statistical Relational Learners : From Benchmarks to Data-Driven Medicine
Natarajan, Sriraam; Kersting, Kristian; Khot, Tushar; Shavlik, Jude
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Boosted Statistical Relational Learners : From Benchmarks to Data-Driven Medicine
Natarajan, Sriraam; Kersting, Kristian; Khot, Tushar; Shavlik, Jude
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2015
Serie: Libro 172 di 322 - SpringerBriefs in Computer Science
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Paperback. Condizione: new. Paperback. 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. This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine (SpringerBriefs in Computer Science)
Natarajan, Sriraam; Kersting, Kristian; Khot, Tushar; Shavlik, Jude
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Boosted Statistical Relational Learners : From Benchmarks to Data-Driven Medicine
Natarajan, Sriraam; Kersting, Kristian; Khot, Tushar; Shavlik, Jude
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Boosted Statistical Relational Learners : From Benchmarks to Data-Driven Medicine
Natarajan, Sriraam; Kersting, Kristian; Khot, Tushar; Shavlik, Jude
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Paperback. Condizione: Brand New. 2014 edition. 74 pages. 9.00x6.00x0.25 inches. In Stock.
Lingua: Inglese
Editore: Springer, Springer, 2015
Serie: Libro 172 di 322 - SpringerBriefs in Computer Science
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Taschenbuch. Condizione: 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 probabili…ty 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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Taschenbuch. Condizione: Neu. Boosted Statistical Relational Learners | From Benchmarks to Data-Driven Medicine | Sriraam Natarajan (u. a.) | Taschenbuch | viii | Englisch | 2015 | Springer | EAN 9783319136431 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]…springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2015
Serie: Libro 172 di 322 - SpringerBriefs in Computer Science
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Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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Paperback. Condizione: new. Paperback. 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. This SpringerBrief addresses the challenges of analyzing multi-relational and noisy data by proposing several Statistical Relational Learning (SRL) methods. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing Mrz 2015, 2015
Serie: Libro 172 di 322 - SpringerBriefs in Computer Science
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: 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 abil…ity 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.
Lingua: Inglese
Editore: Springer International Publishing, 2015
Serie: Libro 172 di 322 - SpringerBriefs in Computer Science
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Da: moluna, Greven, Germaniamoluna
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Condizione: 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 log…ic and the ability of probability theory .
Lingua: Inglese
Editore: Springer, Springer Mär 2015, 2015
Serie: Libro 172 di 322 - SpringerBriefs in Computer Science
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Taschenbuch. Condizione: 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 KG, Sachsenplatz 4-6, 1201 Wien 84 pp. Englisch.
