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Boosted Statistical Relational Learners

From Benchmarks to Data-Driven Medicine

  • Book
  • © 2014

Overview

Authors:
  1. Sriraam Natarajan

    1. Indiana University, Bloomington, USA

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  2. Kristian Kersting

    1. TU Dortmund University, Dortmund, Germany

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  3. Tushar Khot

    1. Indiana University, Bloomington, USA

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  4. Jude Shavlik

    1. University of Wisconsin, Madison, USA

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Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)

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Table of contents (6 chapters)

  1. Front Matter

    Pages i-viii
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  2. Introduction

    • Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude Shavlik
    Pages 1-3

  3. Statistical Relational Learning

    • Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude Shavlik
    Pages 5-17

  4. Boosting (Bi-)Directed Relational Models

    • Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude Shavlik
    Pages 19-26

  5. Boosting Undirected Relational Models

    • Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude Shavlik
    Pages 27-38

  6. Boosting in the Presence of Missing Data

    • Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude Shavlik
    Pages 39-48

  7. Boosting Statistical Relational Learning in Action

    • Sriraam Natarajan, Kristian Kersting, Tushar Khot, Jude Shavlik
    Pages 49-68

  8. Back Matter

    Pages 69-74
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Keywords

About this book

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.

Authors and Affiliations

  • Indiana University, Bloomington, USA

    Sriraam Natarajan, Tushar Khot

  • TU Dortmund University, Dortmund, Germany

    Kristian Kersting

  • University of Wisconsin, Madison, USA

    Jude Shavlik

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