Fr. 76.00

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

Englisch · Taschenbuch

Versand in der Regel in 4 bis 7 Arbeitstagen

Beschreibung

Mehr lesen

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.

Inhaltsverzeichnis

Introduction.- Statistical Relational Learning.- Boosting (Bi-)Directed Relational Models.- Boosting Undirected Relational Models.- Boosting in the presence of missing data.- Boosting Statistical Relational Learning in Action.- Appendix: Booster System.

Zusammenfassung

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.

Produktdetails

Autoren Kristia Kersting, Kristian Kersting, Tushar Khot, Sriraa Natarajan, Sriraam Natarajan, Jude Shavlik
Verlag Springer, Berlin
 
Sprache Englisch
Produktform Taschenbuch
Erschienen 01.01.2015
 
EAN 9783319136431
ISBN 978-3-31-913643-1
Seiten 74
Abmessung 155 mm x 4 mm x 235 mm
Gewicht 143 g
Illustration VIII, 74 p. 25 illus.
Serien SpringerBriefs in Computer Science
SpringerBriefs in Computer Science
Thema Naturwissenschaften, Medizin, Informatik, Technik > Informatik, EDV > Anwendungs-Software

Kundenrezensionen

Zu diesem Artikel wurden noch keine Rezensionen verfasst. Schreibe die erste Bewertung und sei anderen Benutzern bei der Kaufentscheidung behilflich.

Schreibe eine Rezension

Top oder Flop? Schreibe deine eigene Rezension.

Für Mitteilungen an CeDe.ch kannst du das Kontaktformular benutzen.

Die mit * markierten Eingabefelder müssen zwingend ausgefüllt werden.

Mit dem Absenden dieses Formulars erklärst du dich mit unseren Datenschutzbestimmungen einverstanden.