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Phase Transitions in Machine Learning

$108.00 (P)

  • Date Published: July 2011
  • availability: In stock
  • format: Hardback
  • isbn: 9780521763912

$ 108.00 (P)
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About the Authors
  • Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning as well as sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon, and the extensive experimental investigation that supports its presence. They then turn their attention to the possible implications and explore appropriate methods for tackling them. Weaving together fundamental aspects of computer science, statistical physics and machine learning, the book provides sufficient mathematics and physics background to make the subject intelligible to researchers in AI and other computer science communities. Open research issues are also discussed, suggesting promising directions for future research.

    • Deep analysis of foundational issues in machine learning will interest a variety of readers from outside the field, such as cognitive scientists and philosophers
    • Detailed explanations are aided by examples and applications
    • Suitable textbook for graduate-level courses
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    Reviews & endorsements

    "... it is still an open question whether this will be one of the basic tools for understanding machine learning problems and methods in the future. Naturally, this book is an essential source for researchers who want to find answers to these questions."
    Joe Hernandez-Orallo, Computing Reviews

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    Product details

    • Date Published: July 2011
    • format: Hardback
    • isbn: 9780521763912
    • length: 410 pages
    • dimensions: 254 x 195 x 27 mm
    • weight: 1kg
    • contains: 90 b/w illus. 10 tables
    • availability: In stock
  • Table of Contents

    Preface
    Acknowledgements
    Notation
    1. Introduction
    2. Statistical physics and phase transitions
    3. The satisfiability problem
    4. Constraint satisfaction problems
    5. Machine learning
    6. Searching the hypothesis space
    7. Statistical physics and machine learning
    8. Learning, SAT, and CSP
    9. Phase transition in FOL covering test
    10. Phase transitions and relational learning
    11. Phase transitions in grammatical inference
    12. Phase transitions in complex systems
    13. Phase transitions in natural systems
    14. Discussions and open issues
    Appendix A. Phase transitions detected in two real cases
    Appendix B. An intriguing idea
    References
    Index.

  • Authors

    Lorenza Saitta, Università degli Studi del Piemonte Orientale Amedeo Avogadro
    Antoine Cornuéjols is Full Professor of Computer Science at the AgroParisTech Engineering School in Paris.

    Attilio Giordana, Università degli Studi del Piemonte Orientale Amedeo Avogadro
    Attilio Giordana is Full Professor of Computer Science at the University of Piemonte Orientale in Italy.

    Antoine Cornuéjols, AgroParis Tech (INA-PG)
    Lorenza Saitta is a Full Professor of Computer Science at the University of Piemonte Orientale in Italy.

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