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  3. Phase Transitions in Machine Learning
  • Cited by 13
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    • Publisher:
      Cambridge University Press
      Publication date:
      August 2012
      June 2011
      ISBN:
      9780511975509
      9780521763912
      DOI:
      https://doi.org/10.1017/CBO9780511975509
      Dimensions:
      (246 x 189 mm)
      Weight & Pages:
      1.1kg, 410 Pages
      Dimensions:
      Weight & Pages:
      • Subjects:
      Physics and Astronomy, Computer Science, Pattern Recognition and Machine Learning, Statistical Physics
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    Subjects:
    Physics and Astronomy, Computer Science, Pattern Recognition and Machine Learning, Statistical Physics
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    Book description

    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.

    Reviews

    "... 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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