Boosted Statistical Relational Learners: From Benchmarks to Data-Driven Medicine (2014) (SpringerBriefs in Computer Science)

By: and and and

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Synopsis
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.
Copyright:
2014

Book Details

Book Quality:
Publisher Quality
ISBN-13:
9783319136448
Related ISBNs:
9783319136431
Publisher:
Springer International Publishing
Date of Addition:
Copyrighted By:
N/A
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Computers and Internet, Mathematics and Statistics, Medicine
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.

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