EAN: 9783319136431

SPRINGERBRIEFS IN COMPUTER SCIENCE BOOSTED ST...

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.

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