Boosted Statistical Relational Learners : From Benchmarks to Data-Driven Medicine / by Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude Shavlik.

Format:

Book

Author/Creator:

Natarajan, Sriraam, author.

Kersting, Kristian, author.

Khot, Tushar, author.

Shavlik, Jude, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

SpringerBriefs in computer science 2191-5768

SpringerBriefs in Computer Science, 2191-5768

Language:

English

Subjects (All):

Artificial intelligence.

Statistics.

Data mining.

Medical informatics.

Artificial Intelligence.

Statistical Theory and Methods.

Data Mining and Knowledge Discovery.

Health Informatics.

Local Subjects:

Artificial Intelligence.

Statistical Theory and Methods.

Data Mining and Knowledge Discovery.

Health Informatics.

Physical Description:

1 online resource (VIII, 74 pages) : 25 illustrations.

Edition:

First edition 2014.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2014.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-319-13644-8

9783319136448

Access Restriction:

Restricted for use by site license.