Boosted Statistical Relational Learners - From Benchmarks to Data-Driven Medicine (Paperback, 2014 ed.)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	SpringerBriefs in Computer Science
Release date:	March 2015
First published:	2014
Authors:	Sriraam Natarajan • Kristian Kersting • Tushar Khot • Jude Shavlik
Dimensions:	235 x 155 x 4mm (L x W x T)
Format:	Paperback
Pages:	74
Edition:	2014 ed.
ISBN-13:	978-3-319-13643-1
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics Books > Computing & IT > Social & legal aspects of computing > Health & safety aspects of computing Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-319-13643-7
Barcode:	9783319136431

Is the information for this product incomplete, wrong or inappropriate? Let us know about it.

Does this product have an incorrect or missing image? Send us a new image.

Is this product missing categories? Add more categories.

Review This Product

No reviews yet - be the first to create one!