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Algebraic Geometry and Statistical Learning Theory

Algebraic Geometry and Statistical Learning Theory

Algebraic Geometry and Statistical Learning Theory

Sumio Watanabe , Tokyo Institute of Technology
September 2009
Available
Hardback
9780521864671

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    Sure to be influential, Watanabe’s book lays the foundations for the use of algebraic geometry in statistical learning theory. Many models/machines are singular: mixture models, neural networks, HMMs, Bayesian networks, stochastic context-free grammars are major examples. The theory achieved here underpins accurate estimation techniques in the presence of singularities.

    • Presents a new statistical theory for singular learning machines ● Mathematical concepts explained for non-specialists ● Intended for any student interested in machine learning, pattern recognition, artificial intelligence or bioinformatics

    Reviews & endorsements

    "Overall, the many insightful remarks and simple direct language make the book a pleasure to read."
    Shaowei Lin, Mathematical Reviews

    See more reviews

    Product details

    September 2009
    Hardback
    9780521864671
    300 pages
    233 × 155 × 20 mm
    0.56kg
    13 b/w illus.
    Available

    Table of Contents

    • Preface
    • 1. Introduction
    • 2. Singularity theory
    • 3. Algebraic geometry
    • 4. Zeta functions and singular integral
    • 5. Empirical processes
    • 6. Singular learning theory
    • 7. Singular learning machines
    • 8. Singular information science
    • Bibliography
    • Index.
    Resources for
    Type
    Author's web page
      Author
    • Sumio Watanabe , Tokyo Institute of Technology

      Sumio Watanabe is a Professor in the Precision and Intelligence Laboratory at the Tokyo Institute of Technology.